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  • 201.
    Kottravel, Sathish
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Falk, Martin
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Sundén, Erik
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Ropinski, Timo
    Visual Computing Research Group, Ulm University. Germany.
    Coverage-Based Opacity Estimation for Interactive Depth of Field in Molecular Visualization2015In: IEEE Pacific Visualization Symposium (PacificVis 2015), IEEE Computer Society, 2015, p. 255-262Conference paper (Refereed)
    Abstract [en]

    In this paper, we introduce coverage-based opacity estimation to achieve Depth of Field (DoF) effects when visualizing molecular dynamics (MD) data. The proposed algorithm is a novel object-based approach which eliminates many of the shortcomings of state-of-the-art image-based DoF algorithms. Based on observations derived from a physically-correct reference renderer, coverage-based opacity estimation exploits semi-transparency to simulate the blur inherent to DoF effects. It achieves high quality DoF effects, by augmenting each atom with a semi-transparent shell, which has a radius proportional to the distance from the focal plane of the camera. Thus, each shell represents an additional coverage area whose opacity varies radially, based on our observations derived from the results of multi-sampling DoF algorithms. By using the proposed technique, it becomes possible to generate high quality visual results, comparable to those achieved through ground-truth multi-sampling algorithms. At the same time, we obtain a significant speedup which is essential for visualizing MD data as it enables interactive rendering. In this paper, we derive the underlying theory, introduce coverage-based opacity estimation and demonstrate how it can be applied to real world MD data in order to achieve DoF effects. We further analyze the achieved results with respect to performance as well as quality and show that they are comparable to images generated with modern distributed ray tracing engines.

  • 202.
    Krasnoshchekov, Dmitry
    et al.
    Institute for Geospheres Dynamics, Russian Academy of Sciences, Russian Federation.
    Polishchuk, Valentin
    Department of Computer Science, University of Helsinki, Finland.
    Order-k α-hulls and α-shapes2014In: Information Processing Letters, ISSN 0020-0190, Vol. 114, no 1, p. 76-83Article in journal (Refereed)
    Abstract [en]

    We introduce order-k α-hulls and α-shapes – generalizations of α-hulls and α-shapes. Being also a generalization of k-hull (known in statistics as “k-depth contour”), order-k α-hull provides a link between shape reconstruction and statistical depth. As a generalization of α-hull, order-k α-hull gives a robust shape estimation by ignoring locally up to k outliers in a point set. Order-kα-shape produces an “inner” shape of the set, with the amount of “digging” into the points controlled by k. As a generalization of k-hull, order-k α-hull is capable of determining “deep” points amidst samples from a multimodal distribution: it correctly identifies points which lie outside clusters of samples.

    The order-k α-hulls and α-shapes are related to order-k Voronoi diagrams in the same way in which α-hulls and α-shapes are related to Voronoi diagrams. This implies that order-k α-hull and α-shape can be readily built from order-k Voronoi diagram, and that the number of different order-kα-shapes for all possible values of α is proportional to the complexity of order-k Voronoi diagram.

  • 203.
    Kratz, Andrea
    et al.
    Zuse Institute Berlin.
    Auer, Cornelia
    Zuse Institute Berlin.
    Hotz, Ingrid
    Zuse Institue Berlin.
    Tensor Invariants and Glyph Design2014In: Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data / [ed] Carl-Fredrik Westin, Anna Vilanova, Bernhard Burgeth, Springer, 2014, p. 17-34Chapter in book (Refereed)
    Abstract [en]

    Tensors provide a mathematical language for the description of many physical phenomena. They appear everywhere where the dependence of multiple vector fields is approximated as linear. Due to this generality they occur in various application areas, either as result or intermediate product of simulations. As different as these applications, is the physical meaning and relevance of particular mathematical properties. In this context, domain specific tensor invariants that describe the entities of interest play a crucial role. Due to their importance, we propose to build any tensor visualization upon a set of carefully chosen tensor invariants. In this chapter we focus on glyph-based representations, which still belong to the most frequently used tensor visualization methods. For the effectiveness of such visualizations the right choice of glyphs is essential. This chapter summarizes some common glyphs, mostly with origin in mechanical engineering, and link their interpretation to specific tensor invariants.

  • 204.
    Kratz, Andrea
    et al.
    Zuse Institute Berlin.
    Meier, Björn
    Zuse Institue Berlin.
    Hotz, Ingrid
    Zuse Institue Berlin.
    A Visual Approach to Analysis of Stress Tensor Fields2011In: Scientific Visualization: Interactions, Features, Metaphors, Dagstuhl Follow-Ups, ISSN 1868-8977, Vol. 2, p. 188-211Article in journal (Refereed)
    Abstract [en]

    We present a visual approach for the exploration of stress tensor fields. In contrast to common tensor visualization methods that only provide a single view to the tensor field, we pursue the idea of providing various perspectives onto the data in attribute and object space. Especially in the context of stress tensors, advanced tensor visualization methods have a young tradition. Thus, we propose a combination of visualization techniques domain experts are used to with statistical views of tensor attributes. The application of this concept to tensor fields was achieved by extending the notion of shape space. It provides an intuitive way of finding tensor invariants that represent relevant physical properties. Using brushing techniques, the user can select features in attribute space, which are mapped to displayable entities in a three-dimensional hybrid visualization in object space. Volume rendering serves as context, while glyphs encode the whole tensor information in focus regions. Tensorlines can be included to emphasize directionally coherent features in the tensor field. We show that the benefit of such a multi-perspective approach is manifold. Foremost, it provides easy access to the complexity of tensor data. Moreover, including well-known analysis tools, such as Mohr diagrams, users can familiarize themselves gradually with novel visualization methods. Finally, by employing a focus-driven hybrid rendering, we significantly reduce clutter, which was a major problem of other three-dimensional tensor visualization methods. 

  • 205.
    Kristan, Matej
    et al.
    University of Ljubljana, Slovenia.
    Leonardis, Ales
    University of Birmingham, England.
    Matas, Jiri
    Czech Technical University, Czech Republic.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Pflugfelder, Roman
    Austrian Institute Technology, Austria.
    Cehovin, Luka
    University of Ljubljana, Slovenia.
    Vojir, Tomas
    Czech Technical University, Czech Republic.
    Häger, Gustav
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Lukezic, Alan
    University of Ljubljana, Slovenia.
    Fernandez, Gustavo
    Austrian Institute Technology, Austria.
    Gupta, Abhinav
    Carnegie Mellon University, PA 15213 USA.
    Petrosino, Alfredo
    Parthenope University of Naples, Italy.
    Memarmoghadam, Alireza
    University of Isfahan, Iran.
    Garcia-Martin, Alvaro
    University of Autonoma Madrid, Spain.
    Solis Montero, Andres
    University of Ottawa, Canada.
    Vedaldi, Andrea
    University of Oxford, England.
    Robinson, Andreas
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Ma, Andy J.
    Hong Kong Baptist University, Peoples R China.
    Varfolomieiev, Anton
    Kyiv Polytech Institute, Ukraine.
    Alatan, Aydin
    Middle East Technical University, Çankaya, Turkey.
    Erdem, Aykut
    Hacettepe University, Turkey.
    Ghanem, Bernard
    KAUST, Saudi Arabia.
    Liu, Bin
    Moshanghua Technology Co, Peoples R China.
    Han, Bohyung
    POSTECH, South Korea.
    Martinez, Brais
    University of Nottingham, England.
    Chang, Chang-Ming
    University of Albany, GA USA.
    Xu, Changsheng
    Chinese Academic Science, Peoples R China.
    Sun, Chong
    Dalian University of Technology, Peoples R China.
    Kim, Daijin
    POSTECH, South Korea.
    Chen, Dapeng
    Xi An Jiao Tong University, Peoples R China.
    Du, Dawei
    University of Chinese Academic Science, Peoples R China.
    Mishra, Deepak
    Indian Institute Space Science and Technology, India.
    Yeung, Dit-Yan
    Hong Kong University of Science and Technology, Peoples R China.
    Gundogdu, Erhan
    Aselsan Research Centre, Turkey.
    Erdem, Erkut
    Hacettepe University, Turkey.
    Khan, Fahad
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Porikli, Fatih
    ARC Centre Excellence Robot Vis, Australia; Australian National University, Australia; CSIRO, Australia.
    Zhao, Fei
    Chinese Academic Science, Peoples R China.
    Bunyak, Filiz
    University of Missouri, MO 65211 USA.
    Battistone, Francesco
    Parthenope University of Naples, Italy.
    Zhu, Gao
    Australian National University, Australia.
    Roffo, Giorgio
    University of Verona, Italy.
    Sai Subrahmanyam, Gorthi R. K.
    Indian Institute Space Science and Technology, India.
    Bastos, Guilherme
    University of Federal Itajuba, Brazil.
    Seetharaman, Guna
    US Navy, DC 20375 USA.
    Medeiros, Henry
    Marquette University, WI 53233 USA.
    Li, Hongdong
    ARC Centre Excellence Robot Vis, Australia; Australian National University, Australia.
    Qi, Honggang
    University of Chinese Academic Science, Peoples R China.
    Bischof, Horst
    Graz University of Technology, Austria.
    Possegger, Horst
    Graz University of Technology, Austria.
    Lu, Huchuan
    Dalian University of Technology, Peoples R China.
    Lee, Hyemin
    POSTECH, South Korea.
    Nam, Hyeonseob
    NAVER Corp, South Korea.
    Jin Chang, Hyung
    Imperial Coll London, England.
    Drummond, Isabela
    University of Federal Itajuba, Brazil.
    Valmadre, Jack
    University of Oxford, England.
    Jeong, Jae-chan
    ASRI, South Korea; Elect and Telecommun Research Institute, South Korea.
    Cho, Jae-il
    Elect and Telecommun Research Institute, South Korea.
    Lee, Jae-Yeong
    Elect and Telecommun Research Institute, South Korea.
    Zhu, Jianke
    Zhejiang University, Peoples R China.
    Feng, Jiayi
    Chinese Academic Science, Peoples R China.
    Gao, Jin
    Chinese Academic Science, Peoples R China.
    Young Choi, Jin
    ASRI, South Korea.
    Xiao, Jingjing
    University of Birmingham, England.
    Kim, Ji-Wan
    Elect and Telecommun Research Institute, South Korea.
    Jeong, Jiyeoup
    ASRI, South Korea; Elect and Telecommun Research Institute, South Korea.
    Henriques, Joao F.
    University of Oxford, England.
    Lang, Jochen
    University of Ottawa, Canada.
    Choi, Jongwon
    ASRI, South Korea.
    Martinez, Jose M.
    University of Autonoma Madrid, Spain.
    Xing, Junliang
    Chinese Academic Science, Peoples R China.
    Gao, Junyu
    Chinese Academic Science, Peoples R China.
    Palaniappan, Kannappan
    University of Missouri, MO 65211 USA.
    Lebeda, Karel
    University of Surrey, England.
    Gao, Ke
    University of Missouri, MO 65211 USA.
    Mikolajczyk, Krystian
    Imperial Coll London, England.
    Qin, Lei
    Chinese Academic Science, Peoples R China.
    Wang, Lijun
    Dalian University of Technology, Peoples R China.
    Wen, Longyin
    University of Albany, GA USA.
    Bertinetto, Luca
    University of Oxford, England.
    Kumar Rapuru, Madan
    Indian Institute Space Science and Technology, India.
    Poostchi, Mahdieh
    University of Missouri, MO 65211 USA.
    Maresca, Mario
    Parthenope University of Naples, Italy.
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Mueller, Matthias
    KAUST, Saudi Arabia.
    Zhang, Mengdan
    Chinese Academic Science, Peoples R China.
    Arens, Michael
    Fraunhofer IOSB, Germany.
    Valstar, Michel
    University of Nottingham, England.
    Tang, Ming
    Chinese Academic Science, Peoples R China.
    Baek, Mooyeol
    POSTECH, South Korea.
    Haris Khan, Muhammad
    University of Nottingham, England.
    Wang, Naiyan
    Hong Kong University of Science and Technology, Peoples R China.
    Fan, Nana
    Harbin Institute Technology, Peoples R China.
    Al-Shakarji, Noor
    University of Missouri, MO 65211 USA.
    Miksik, Ondrej
    University of Oxford, England.
    Akin, Osman
    Hacettepe University, Turkey.
    Moallem, Payman
    University of Isfahan, Iran.
    Senna, Pedro
    University of Federal Itajuba, Brazil.
    Torr, Philip H. S.
    University of Oxford, England.
    Yuen, Pong C.
    Hong Kong Baptist University, Peoples R China.
    Huang, Qingming
    Harbin Institute Technology, Peoples R China; University of Chinese Academic Science, Peoples R China.
    Martin-Nieto, Rafael
    University of Autonoma Madrid, Spain.
    Pelapur, Rengarajan
    University of Missouri, MO 65211 USA.
    Bowden, Richard
    University of Surrey, England.
    Laganiere, Robert
    University of Ottawa, Canada.
    Stolkin, Rustam
    University of Birmingham, England.
    Walsh, Ryan
    Marquette University, WI 53233 USA.
    Krah, Sebastian B.
    Fraunhofer IOSB, Germany.
    Li, Shengkun
    Hong Kong University of Science and Technology, Peoples R China; University of Albany, GA USA.
    Zhang, Shengping
    Harbin Institute Technology, Peoples R China.
    Yao, Shizeng
    University of Missouri, MO 65211 USA.
    Hadfield, Simon
    University of Surrey, England.
    Melzi, Simone
    University of Verona, Italy.
    Lyu, Siwei
    University of Albany, GA USA.
    Li, Siyi
    Hong Kong University of Science and Technology, Peoples R China; University of Albany, GA USA.
    Becker, Stefan
    Fraunhofer IOSB, Germany.
    Golodetz, Stuart
    University of Oxford, England.
    Kakanuru, Sumithra
    Indian Institute Space Science and Technology, India.
    Choi, Sunglok
    Elect and Telecommun Research Institute, South Korea.
    Hu, Tao
    University of Chinese Academic Science, Peoples R China.
    Mauthner, Thomas
    Graz University of Technology, Austria.
    Zhang, Tianzhu
    Chinese Academic Science, Peoples R China.
    Pridmore, Tony
    University of Nottingham, England.
    Santopietro, Vincenzo
    Parthenope University of Naples, Italy.
    Hu, Weiming
    Chinese Academic Science, Peoples R China.
    Li, Wenbo
    Lehigh University, PA 18015 USA.
    Huebner, Wolfgang
    Fraunhofer IOSB, Germany.
    Lan, Xiangyuan
    Hong Kong Baptist University, Peoples R China.
    Wang, Xiaomeng
    University of Nottingham, England.
    Li, Xin
    Harbin Institute Technology, Peoples R China.
    Li, Yang
    Zhejiang University, Peoples R China.
    Demiris, Yiannis
    Imperial Coll London, England.
    Wang, Yifan
    Dalian University of Technology, Peoples R China.
    Qi, Yuankai
    Harbin Institute Technology, Peoples R China.
    Yuan, Zejian
    Xi An Jiao Tong University, Peoples R China.
    Cai, Zexiong
    Hong Kong Baptist University, Peoples R China.
    Xu, Zhan
    Zhejiang University, Peoples R China.
    He, Zhenyu
    Harbin Institute Technology, Peoples R China.
    Chi, Zhizhen
    Dalian University of Technology, Peoples R China.
    The Visual Object Tracking VOT2016 Challenge Results2016In: COMPUTER VISION - ECCV 2016 WORKSHOPS, PT II, SPRINGER INT PUBLISHING AG , 2016, Vol. 9914, p. 777-823Conference paper (Refereed)
    Abstract [en]

    The Visual Object Tracking challenge VOT2016 aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 70 trackers are presented, with a large number of trackers being published at major computer vision conferences and journals in the recent years. The number of tested state-of-the-art trackers makes the VOT 2016 the largest and most challenging benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the Appendix. The VOT2016 goes beyond its predecessors by (i) introducing a new semi-automatic ground truth bounding box annotation methodology and (ii) extending the evaluation system with the no-reset experiment.

  • 206.
    Kristan, Matej
    et al.
    University of Ljubljana, Slovenia.
    Matas, Jiri
    Czech Technical University, Czech Republic.
    Leonardis, Ales
    University of Birmingham, England.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Cehovin, Luka
    University of Ljubljana, Slovenia.
    Fernandez, Gustavo
    Austrian Institute Technology, Austria.
    Vojir, Tomas
    Czech Technical University, Czech Republic.
    Häger, Gustav
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Nebehay, Georg
    Austrian Institute Technology, Austria.
    Pflugfelder, Roman
    Austrian Institute Technology, Austria.
    Gupta, Abhinav
    Carnegie Mellon University, PA 15213 USA.
    Bibi, Adel
    King Abdullah University of Science and Technology, Saudi Arabia.
    Lukezic, Alan
    University of Ljubljana, Slovenia.
    Garcia-Martins, Alvaro
    University of Autonoma Madrid, Spain.
    Saffari, Amir
    Affectv, England.
    Petrosino, Alfredo
    Parthenope University of Naples, Italy.
    Solis Montero, Andres
    University of Ottawa, Canada.
    Varfolomieiev, Anton
    National Technical University of Ukraine, Ukraine.
    Baskurt, Atilla
    University of Lyon, France.
    Zhao, Baojun
    Beijing Institute Technology, Peoples R China.
    Ghanem, Bernard
    King Abdullah University of Science and Technology, Saudi Arabia.
    Martinez, Brais
    University of Nottingham, England.
    Lee, ByeongJu
    Seoul National University, South Korea.
    Han, Bohyung
    POSTECH, South Korea.
    Wang, Chaohui
    University of Paris Est, France.
    Garcia, Christophe
    LIRIS, France.
    Zhang, Chunyuan
    National University of Def Technology, Peoples R China; National Key Lab Parallel and Distributed Proc, Peoples R China.
    Schmid, Cordelia
    INRIA Grenoble Rhone Alpes, France.
    Tao, Dacheng
    University of Technology Sydney, Australia.
    Kim, Daijin
    POSTECH, South Korea.
    Huang, Dafei
    National University of Def Technology, Peoples R China; National Key Lab Parallel and Distributed Proc, Peoples R China.
    Prokhorov, Danil
    Toyota Research Institute, Japan.
    Du, Dawei
    SUNY Albany, NY USA; Chinese Academic Science, Peoples R China.
    Yeung, Dit-Yan
    Hong Kong University of Science and Technology, Peoples R China.
    Ribeiro, Eraldo
    Florida Institute Technology, FL USA.
    Khan, Fahad
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Porikli, Fatih
    Australian National University, Australia; NICTA, Australia.
    Bunyak, Filiz
    University of Missouri, MO 65211 USA.
    Zhu, Gao
    Australian National University, Australia.
    Seetharaman, Guna
    Naval Research Lab, DC 20375 USA.
    Kieritz, Hilke
    Fraunhofer IOSB, Germany.
    Tuen Yau, Hing
    Chinese University of Hong Kong, Peoples R China.
    Li, Hongdong
    Australian National University, Australia; ARC Centre Excellence Robot Vis, Australia.
    Qi, Honggang
    SUNY Albany, NY USA; Chinese Academic Science, Peoples R China.
    Bischof, Horst
    Graz University of Technology, Austria.
    Possegger, Horst
    Graz University of Technology, Austria.
    Lee, Hyemin
    POSTECH, South Korea.
    Nam, Hyeonseob
    POSTECH, South Korea.
    Bogun, Ivan
    Florida Institute Technology, FL USA.
    Jeong, Jae-chan
    Elect and Telecommun Research Institute, South Korea.
    Cho, Jae-il
    Elect and Telecommun Research Institute, South Korea.
    Lee, Jae-Young
    Elect and Telecommun Research Institute, South Korea.
    Zhu, Jianke
    Zhejiang University, Peoples R China.
    Shi, Jianping
    CUHK, Peoples R China.
    Li, Jiatong
    Beijing Institute Technology, Peoples R China; University of Technology Sydney, Australia.
    Jia, Jiaya
    CUHK, Peoples R China.
    Feng, Jiayi
    Chinese Academic Science, Peoples R China.
    Gao, Jin
    Chinese Academic Science, Peoples R China.
    Young Choi, Jin
    Seoul National University, South Korea.
    Kim, Ji-Wan
    Elect and Telecommun Research Institute, South Korea.
    Lang, Jochen
    University of Ottawa, Canada.
    Martinez, Jose M.
    University of Autonoma Madrid, Spain.
    Choi, Jongwon
    Seoul National University, South Korea.
    Xing, Junliang
    Chinese Academic Science, Peoples R China.
    Xue, Kai
    Harbin Engn University, Peoples R China.
    Palaniappan, Kannappan
    University of Missouri, MO 65211 USA.
    Lebeda, Karel
    University of Surrey, England.
    Alahari, Karteek
    INRIA Grenoble Rhone Alpes, France.
    Gao, Ke
    University of Missouri, MO 65211 USA.
    Yun, Kimin
    Seoul National University, South Korea.
    Hong Wong, Kin
    Chinese University of Hong Kong, Peoples R China.
    Luo, Lei
    National University of Def Technology, Peoples R China.
    Ma, Liang
    Harbin Engn University, Peoples R China.
    Ke, Lipeng
    SUNY Albany, NY USA; Chinese Academic Science, Peoples R China.
    Wen, Longyin
    SUNY Albany, NY USA.
    Bertinetto, Luca
    University of Oxford, England.
    Pootschi, Mandieh
    University of Missouri, MO 65211 USA.
    Maresca, Mario
    Parthenope University of Naples, Italy.
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Wen, Mei
    National University of Def Technology, Peoples R China; National Key Lab Parallel and Distributed Proc, Peoples R China.
    Zhang, Mengdan
    Chinese Academic Science, Peoples R China.
    Arens, Michael
    Fraunhofer IOSB, Germany.
    Valstar, Michel
    University of Nottingham, England.
    Tang, Ming
    Chinese Academic Science, Peoples R China.
    Chang, Ming-Ching
    SUNY Albany, NY USA.
    Haris Khan, Muhammad
    University of Nottingham, England.
    Fan, Nana
    Harbin Institute Technology, Peoples R China.
    Wang, Naiyan
    TuSimple LLC, CA USA; Hong Kong University of Science and Technology, Peoples R China.
    Miksik, Ondrej
    University of Oxford, England.
    Torr, Philip H. S.
    University of Oxford, England.
    Wang, Qiang
    Chinese Academic Science, Peoples R China.
    Martin-Nieto, Rafael
    University of Autonoma Madrid, Spain.
    Pelapur, Rengarajan
    University of Missouri, MO 65211 USA.
    Bowden, Richard
    University of Surrey, England.
    Laganiere, Robert
    University of Ottawa, Canada.
    Moujtahid, Salma
    University of Lyon, France.
    Hare, Sam
    Obvious Engn, England.
    Hadfield, Simon
    University of Surrey, England.
    Lyu, Siwei
    SUNY Albany, NY USA.
    Li, Siyi
    Hong Kong University of Science and Technology, Peoples R China.
    Zhu, Song-Chun
    University of California, USA.
    Becker, Stefan
    Fraunhofer IOSB, Germany.
    Duffner, Stefan
    University of Lyon, France; LIRIS, France.
    Hicks, Stephen L.
    University of Oxford, England.
    Golodetz, Stuart
    University of Oxford, England.
    Choi, Sunglok
    Elect and Telecommun Research Institute, South Korea.
    Wu, Tianfu
    University of California, USA.
    Mauthner, Thomas
    Graz University of Technology, Austria.
    Pridmore, Tony
    University of Nottingham, England.
    Hu, Weiming
    Chinese Academic Science, Peoples R China.
    Hubner, Wolfgang
    Fraunhofer IOSB, Germany.
    Wang, Xiaomeng
    University of Nottingham, England.
    Li, Xin
    Harbin Institute Technology, Peoples R China.
    Shi, Xinchu
    Chinese Academic Science, Peoples R China.
    Zhao, Xu
    Chinese Academic Science, Peoples R China.
    Mei, Xue
    Toyota Research Institute, Japan.
    Shizeng, Yao
    University of Missouri, USA.
    Hua, Yang
    INRIA Grenoble Rhône-Alpes, France.
    Li, Yang
    Zhejiang University, Peoples R China.
    Lu, Yang
    University of California, USA.
    Li, Yuezun
    SUNY Albany, NY USA.
    Chen, Zhaoyun
    National University of Def Technology, Peoples R China; National Key Lab Parallel and Distributed Proc, Peoples R China.
    Huang, Zehua
    Carnegie Mellon University, PA 15213 USA.
    Chen, Zhe
    University of Technology Sydney, Australia.
    Zhang, Zhe
    Baidu Corp, Peoples R China.
    He, Zhenyu
    Harbin Institute Technology, Peoples R China.
    Hong, Zhibin
    University of Technology Sydney, Australia.
    The Visual Object Tracking VOT2015 challenge results2015In: Proceedings 2015 IEEE International Conference on Computer Vision Workshops ICCVW 2015, IEEE , 2015, p. 564-586Conference paper (Refereed)
    Abstract [en]

    The Visual Object Tracking challenge 2015, VOT2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 62 trackers are presented. The number of tested trackers makes VOT 2015 the largest benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the appendix. Features of the VOT2015 challenge that go beyond its VOT2014 predecessor are: (i) a new VOT2015 dataset twice as large as in VOT2014 with full annotation of targets by rotated bounding boxes and per-frame attribute, (ii) extensions of the VOT2014 evaluation methodology by introduction of a new performance measure. The dataset, the evaluation kit as well as the results are publicly available at the challenge website(1).

  • 207.
    Kristan, Matej
    et al.
    University of Ljubljana, Ljubljana, Slovenia.
    Pflugfelder, Roman P.
    Austrian Institute of Technology, Vienna, Austria.
    Leonardis, Ales
    University of Birmingham, Birmingham, UK.
    Matas, Jiri
    Czech Technical University, Prague, Czech Republic.
    Cehovin, Luka
    University of Ljubljana, Ljubljana, Slovenia.
    Nebehay, Georg
    Austrian Institute of Technology, Vienna, Austria.
    Vojir, Tomas
    Czech Technical University, Prague, Czech Republic.
    Fernandez, Gustavo
    Austrian Institute of Technology, Vienna, Austria.
    Lukezi, Alan
    University of Ljubljana, Ljubljana, Slovenia.
    Dimitriev, Aleksandar
    University of Ljubljana, Ljubljana, Slovenia.
    Petrosino, Alfredo
    Parthenope University of Naples, Naples, Italy.
    Saffari, Amir
    Affectv Limited, London, UK.
    Li, Bo
    Panasonic R&D Center, Singapore, Singapore.
    Han, Bohyung
    POSTECH, Pohang, Korea.
    Heng, CherKeng
    Panasonic R&D Center, Singapore, Singapore.
    Garcia, Christophe
    LIRIS, Lyon, France.
    Pangersic, Dominik
    University of Ljubljana, Ljubljana, Slovenia.
    Häger, Gustav
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Oven, Franci
    University of Ljubljana, Ljubljana, Slovenia.
    Possegger, Horst
    Graz University of Technology, Graz, Austria.
    Bischof, Horst
    Graz University of Technology, Graz, Austria.
    Nam, Hyeonseob
    POSTECH, Pohang, Korea.
    Zhu, Jianke
    Zhejiang University, Hangzhou, China.
    Li, JiJia
    Shanghai Jiao Tong University, Shanghai, China.
    Choi, Jin Young
    ASRI Seoul National University, Gwanak, Korea.
    Choi, Jin-Woo
    Electronics and Telecommunications Research Institute, Daejeon, Korea.
    Henriques, Joao F.
    University of Coimbra, Coimbra, Portugal.
    van de Weijer, Joost
    Universitat Autonoma de Barcelona, Barcelona, Spain.
    Batista, Jorge
    University of Coimbra, Coimbra, Portugal.
    Lebeda, Karel
    University of Surrey, Surrey, UK.
    Ofjall, Kristoffer
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Yi, Kwang Moo
    EPFL CVLab, Lausanne, Switzerland.
    Qin, Lei
    ICT CAS, Beijing, China.
    Wen, Longyin
    Chinese Academy of Sciences, Beijing, China.
    Maresca, Mario Edoardo
    Parthenope University of Naples, Naples, Italy.
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Cheng, Ming-Ming
    University of Oxford, Oxford, UK.
    Torr, Philip
    University of Oxford, Oxford, UK.
    Huang, Qingming
    Harbin Institute of Technology, Harbin, China.
    Bowden, Richard
    University of Surrey, Surrey, UK.
    Hare, Sam
    Obvious Engineering Limited, London, UK.
    YueYing Lim, Samantha
    Panasonic R&D Center, Singapore, Singapore.
    Hong, Seunghoon
    POSTECH, Pohang, Korea.
    Liao, Shengcai
    Chinese Academy of Sciences, Beijing, China.
    Hadfield, Simon
    University of Surrey, Surrey, UK.
    Li, Stan Z.
    Chinese Academy of Sciences, Beijing, China.
    Duffner, Stefan
    LIRIS, Lyon, France.
    Golodetz, Stuart
    University of Oxford, Oxford, UK.
    Mauthner, Thomas
    Graz University of Technology, Graz, Austria.
    Vineet, Vibhav
    University of Oxford, Oxford, UK.
    Lin, Weiyao
    Shanghai Jiao Tong University, Shanghai, China.
    Li, Yang
    Zhejiang University, Hangzhou, China.
    Qi, Yuankai
    Harbin Institute of Technology, Harbin, China.
    Lei, Zhen
    Chinese Academy of Sciences, Beijing, China.
    Niu, ZhiHeng
    Panasonic R&D Center, Singapore, Singapore.
    The Visual Object Tracking VOT2014 Challenge Results2015In: COMPUTER VISION - ECCV 2014 WORKSHOPS, PT II, Springer, 2015, Vol. 8926, p. 191-217Conference paper (Refereed)
    Abstract [en]

    The Visual Object Tracking challenge 2014, VOT2014, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 38 trackers are presented. The number of tested trackers makes VOT 2014 the largest benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the appendix. Features of the VOT2014 challenge that go beyond its VOT2013 predecessor are introduced: (i) a new VOT2014 dataset with full annotation of targets by rotated bounding boxes and per-frame attribute, (ii) extensions of the VOT2013 evaluation methodology, (iii) a new unit for tracking speed assessment less dependent on the hardware and (iv) the VOT2014 evaluation toolkit that significantly speeds up execution of experiments. The dataset, the evaluation kit as well as the results are publicly available at the challenge website (http://​votchallenge.​net).

  • 208.
    Lam, Benny
    et al.
    Linköping University, Department of Computer and Information Science.
    Nilsson, Jakob
    Linköping University, Department of Computer and Information Science.
    Creating Good User Experience in a Hand-Gesture-Based Augmented Reality Game2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The dissemination of new innovative technology requires feasibility and simplicity. The problem with marker-based augmented reality is similar to glove-based hand gesture recognition: they both require an additional component to function. This thesis investigates the possibility of combining markerless augmented reality together with appearance-based hand gesture recognition by implementing a game with good user experience.

    The methods employed in this research consist of a game implementation and a pre-study meant for measuring interactive accuracy and precision, and for deciding upon which gestures should be utilized in the game. A test environment was realized in Unity using ARKit and Manomotion SDK. Similarly, the implementation of the game used the same development tools. However, Blender was used for creating the 3D models.

    The results from 15 testers showed that the pinching gesture was the most favorable one. The game was evaluated with a System Usability Scale (SUS) and received a score of 70.77 among 12 game testers, which indicates that the augmented reality game, which interaction method is solely based on bare-hands, can be quite enjoyable.

  • 209.
    Landgård, Jonas
    Linköping University, Department of Electrical Engineering.
    Segmentering och klassificering av LiDAR-data2005Independent thesis Basic level (professional degree), 20 points / 30 hpStudent thesis
    Abstract [en]

    With numerous applications in both military and civilian life, the demand for accurate 3D models of real world environments increases rapidly. Using an airborne laser scanner for the raw data acquisition and robust methods for data processing, the researchers at the Swedish Defence Research Agency (FOI) in Linköping hope to fully automate the modeling process.

    The work of this thesis has mainly been focused on three areas: ground estimation, image segmentation and classification. Procedures have in each of these areas been developed, leading to a new algorithm for ground estimation, a number of segmentation methods as well as a full comparison of various decision values for an object based classification. The ground estimation algorithm developed has yielded good results compared to the method based on active contours previously elaborated at FOI. The computational effort needed by the new method has been greatly reduced compared to the former, as performance, particularly in urban areas, has been improved. The segmentation methods introduced have shown promising results in separating different types of objects. A new set of decision values and descriptors for the object based classifier has been suggested, which, according to tests, prove to be more efficient than the set p reviously used.

  • 210.
    Landén, David
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Complex Task Allocation for Delegation: From Theory to Practice2011Licentiate thesis, monograph (Other academic)
    Abstract [en]

    The problem of determining who should do what given a set of tasks and a set of agents is called the task allocation problem. The problem occurs in many multi-agent system applications where a workload of tasks should be shared by a number of agents. In our case, the task allocation problem occurs as an integral part of a larger problem of determining if a task can be delegated from one agent to another.

    Delegation is the act of handing over the responsibility for something to someone. Previously, a theory for delegation including a delegation speech act has been specified. The speech act specifies the preconditions that must be fulfilled before the delegation can be carried out, and the postconditions that will be true afterward. To actually use the speech act in a multi-agent system, there must be a practical way of determining if the preconditions are true. This can be done by a process that includes solving a complex task allocation problem by the agents involved in the delegation.

    In this thesis a constraint-based task specification formalism, a complex task allocation algorithm for allocating tasks to unmanned aerial vehicles and a generic collaborative system shell for robotic systems are developed. The three components are used as the basis for a collaborative unmanned aircraft system that uses delegation for distributing and coordinating the agents' execution of complex tasks.

  • 211.
    Larsson, Fredrik
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Shape Based Recognition – Cognitive Vision Systems in Traffic Safety Applications2011Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Traffic accidents are globally the number one cause of death for people 15-29 years old and is among the top three causes for all age groups 5-44 years. Much of the work within this thesis has been carried out in projects aiming for (cognitive) driver assistance systems and hopefully represents a step towards improving traffic safety.

    The main contributions are within the area of Computer Vision, and more specifically, within the areas of shape matching, Bayesian tracking, and visual servoing with the main focus being on shape matching and applications thereof. The different methods have been demonstrated in traffic safety applications, such as  bicycle tracking, car tracking, and traffic sign recognition, as well as for pose estimation and robot control.

    One of the core contributions is a new method for recognizing closed contours, based on complex correlation of Fourier descriptors. It is shown that keeping the phase of Fourier descriptors is important. Neglecting the phase can result in perfect matches between intrinsically different shapes. Another benefit of keeping the phase is that rotation covariant or invariant matching is achieved in the same way. The only difference is to either consider the magnitude, for rotation invariant matching, or just the real value, for rotation covariant matching, of the complex valued correlation.

    The shape matching method has further been used in combination with an implicit star-shaped object model for traffic sign recognition. The presented method works fully automatically on query images with no need for regions-of-interests. It is shown that the presented method performs well for traffic signs that contain multiple distinct contours, while some improvement still is needed for signs defined by a single contour. The presented methodology is general enough to be used for arbitrary objects, as long as they can be defined by a number of regions.

    Another contribution has been the extension of a framework for learning based Bayesian tracking called channel based tracking. Compared to earlier work, the multi-dimensional case has been reformulated in a sound probabilistic way and the learning algorithm itself has been extended. The framework is evaluated in car tracking scenarios and is shown to give competitive tracking performance, compared to standard approaches, but with the advantage of being fully learnable.

    The last contribution has been in the field of (cognitive) robot control. The presented method achieves sufficient accuracy for simple assembly tasks by combining autonomous recognition with visual servoing, based on a learned mapping between percepts and actions. The method demonstrates that limitations of inexpensive hardware, such as web cameras and low-cost robotic arms, can be overcome using powerful algorithms.

    All in all, the methods developed and presented in this thesis can all be used for different components in a system guided by visual information, and hopefully represents a step towards improving traffic safety.

    List of papers
    1. Torchlight Navigation
    Open this publication in new window or tab >>Torchlight Navigation
    Show others...
    2010 (English)In: Proceedings of the 20th International Conferenceon Pattern Recognition, 2010, p. 302-306Conference paper, Published paper (Refereed)
    Abstract [en]

    A common computer vision task is navigation and mapping. Many indoor navigation tasks require depth knowledge of flat, unstructured surfaces (walls, floor, ceiling). With passive illumination only, this is an ill-posed problem. Inspired by small children using a torchlight, we use a spotlight for active illumination. Using our torchlight approach, depth and orientation estimation of unstructured, flat surfaces boils down to estimation of ellipse parameters. The extraction of ellipses is very robust and requires little computational effort.

    Series
    International Conference on Pattern Recognition, ISSN 1051-4651
    Keywords
    Torchlight, Pose estimation, Active illumination, Plane estimation, Ellipses
    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:liu:diva-60597 (URN)10.1109/ICPR.2010.83 (DOI)978-1-4244-7542-1 (ISBN)978-0-7695-4109-9 (ISBN)
    Conference
    20th International Conference on Pattern Recognition, Istanbul, Turkey, 23-26 August, 2010
    Projects
    DIPLECSGARNICSELLIITCADICS
    Funder
    Swedish Foundation for Strategic Research
    Available from: 2010-10-20 Created: 2010-10-20 Last updated: 2016-05-04Bibliographically approved
    2. Bicycle Tracking Using Ellipse Extraction
    Open this publication in new window or tab >>Bicycle Tracking Using Ellipse Extraction
    Show others...
    2011 (English)In: Proceedings of the 14thInternational Conference on Information Fusion, 2011, IEEE , 2011, p. 1-8Conference paper, Published paper (Refereed)
    Abstract [en]

    A new approach to track bicycles from imagery sensor data is proposed. It is based on detecting ellipsoids in the images, and treat these pair-wise using a dynamic bicycle model. One important application area is in automotive collision avoidance systems, where no dedicated systems for bicyclists yet exist and where very few theoretical studies have been published.

    Possible conflicts can be predicted from the position and velocity state in the model, but also from the steering wheel articulation and roll angle that indicate yaw changes before the velocity vector changes. An algorithm is proposed which consists of an ellipsoid detection and estimation algorithm and a particle filter.

    A simulation study of three critical single target scenarios is presented, and the algorithm is shown to produce excellent state estimates. An experiment using a stationary camera and the particle filter for state estimation is performed and has shown encouraging results.

    Place, publisher, year, edition, pages
    IEEE, 2011
    Keywords
    Tracking, Particle Filter, Computer Vision, Ellipse Extraction, Bicycle
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-69672 (URN)978-1-4577-0267-9 (ISBN)
    Conference
    The 14th International Conference on Information Fusion, 5-8 July 2011, Chicago, IL, USA
    Available from: 2011-07-13 Created: 2011-07-13 Last updated: 2016-05-04Bibliographically approved
    3. Correlating Fourier descriptors of local patches for road sign recognition
    Open this publication in new window or tab >>Correlating Fourier descriptors of local patches for road sign recognition
    2011 (English)In: IET Computer Vision, ISSN 1751-9632, E-ISSN 1751-9640, Vol. 5, no 4, p. 244-254Article in journal (Refereed) Published
    Abstract [en]

    The Fourier descriptors (FDs) is a classical but still popular method for contour matching. The key idea is to apply the Fourier transform to a periodic representation of the contour, which results in a shape descriptor in the frequency domain. FDs are most commonly used to compare object silhouettes and object contours; the authors instead use this well-established machinery to describe local regions to be used in an object-recognition framework. Many approaches to matching FDs are based on the magnitude of each FD component, thus ignoring the information contained in the phase. Keeping the phase information requires us to take into account the global rotation of the contour and shifting of the contour samples. The authors show that the sum-of-squared differences of FDs can be computed without explicitly de-rotating the contours. The authors compare correlation-based matching against affine-invariant Fourier descriptors (AFDs) and WARP-matched FDs and demonstrate that correlation-based approach outperforms AFDs and WARP on real data. As a practical application the authors demonstrate the proposed correlation-based matching on a road sign recognition task.

    Place, publisher, year, edition, pages
    IET, 2011
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-65621 (URN)10.1049/iet-cvi.2010.0040 (DOI)000291385900007 ()
    Projects
    DIPLECS, GARNICS, ELLIIT
    Note
    This paper is a postprint of a paper submitted to and accepted for publication in IET Computer Vision and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library Fredrik Larsson, Michael Felsberg and Per-Erik Forssen, Correlating Fourier descriptors of local patches for road sign recognition, 2011, IET Computer Vision, (5), 4, 244-254. http://dx.doi.org/10.1049/iet-cvi.2010.0040 Copyright: Iet http://www.theiet.org/ Available from: 2011-02-14 Created: 2011-02-14 Last updated: 2017-12-11Bibliographically approved
    4. Using Fourier Descriptors and Spatial Models for Traffic Sign Recognition
    Open this publication in new window or tab >>Using Fourier Descriptors and Spatial Models for Traffic Sign Recognition
    2011 (English)In: Image Analysis: 17th Scandinavian Conference, SCIA 2011, Ystad, Sweden, May 2011. Proceedings / [ed] Anders Heyden, Fredrik Kahl, Springer Berlin/Heidelberg, 2011, p. 238-249Conference paper, Published paper (Refereed)
    Abstract [en]

    Traffic sign recognition is important for the development of driver assistance systems and fully autonomous vehicles. Even though GPS navigator systems works well for most of the time, there will always be situations when they fail. In these cases, robust vision based systems are required. Traffic signs are designed to have distinct colored fields separated by sharp boundaries. We propose to use locally segmented contours combined with an implicit star-shaped object model as prototypes for the different sign classes. The contours are described by Fourier descriptors. Matching of a query image to the sign prototype database is done by exhaustive search. This is done efficiently by using the correlation based matching scheme for Fourier descriptors and a fast cascaded matching scheme for enforcing the spatial requirements. We demonstrated on a publicly available database state of the art performance.

    Place, publisher, year, edition, pages
    Springer Berlin/Heidelberg, 2011
    Series
    Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 6688
    Keywords
    Traffic sign recognition – Fourier descriptors – spatial models – traffic sign dataset
    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:liu:diva-69521 (URN)10.1007/978-3-642-21227-7_23 (DOI)000308543900023 ()978-3-642-21226-0 (ISBN)978-3-642-21227-7 (ISBN)
    Conference
    17th Scandinavian Conference on Image Analysis (SCIA), Ystad, Sweden, May 23-27, 2011
    Note

    Original Publication: Fredrik Larsson and Michael Felsberg, Using Fourier Descriptors and Spatial Models for Traffic Sign Recognition, SCIA konferens, 23-27 May 2011, Ystad Sweden, 2011, Lecture Notes in Computer Science, Image Analysis, 238-249. http://dx.doi.org/10.1007/978-3-642-21227-7_23 Copyright: Springer

    Available from: 2011-06-30 Created: 2011-06-30 Last updated: 2018-01-30Bibliographically approved
    5. Learning Higher-Order Markov Models for ObjectTracking in Image Sequences
    Open this publication in new window or tab >>Learning Higher-Order Markov Models for ObjectTracking in Image Sequences
    2009 (English)In: Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II, Berlin, Heidelberg: Springer-Verlag , 2009, p. 184-195Conference paper, Published paper (Refereed)
    Abstract [en]

    This work presents a novel object tracking approach, where the motion model is learned from sets of frame-wise detections with unknown associations. We employ a higher-order Markov model on position space instead of a first-order Markov model on a high-dimensional state-space of object dynamics. Compared to the latter, our approach allows the use of marginal rather than joint distributions, which results in a significant reduction of computation complexity. Densities are represented using a grid-based approach, where the rectangular windows are replaced with estimated smooth Parzen windows sampled at the grid points. This method performs as accurately as particle filter methods with the additional advantage that the prediction and update steps can be learned from empirical data. Our method is compared against standard techniques on image sequences obtained from an RC car following scenario. We show that our approach performs best in most of the sequences. Other potential applications are surveillance from cheap or uncalibrated cameras and image sequence analysis.

    Place, publisher, year, edition, pages
    Berlin, Heidelberg: Springer-Verlag, 2009
    Series
    Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 5876
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-50495 (URN)10.1007/978-3-642-10520-3_17 (DOI)000279247100017 ()978-3-642-10519-7 (ISBN)
    Conference
    The 5th International Symposium on Advances in Visual Computing (ISVC), Las Vegas, USA, December
    Projects
    DIPLECS
    Available from: 2009-10-12 Created: 2009-10-12 Last updated: 2018-01-31Bibliographically approved
    6. Simultaneously learning to recognize and control a low-cost robotic arm
    Open this publication in new window or tab >>Simultaneously learning to recognize and control a low-cost robotic arm
    2009 (English)In: Image and Vision Computing, ISSN 0262-8856, E-ISSN 1872-8138, Vol. 27, no 11, p. 1729-1739Article in journal (Refereed) Published
    Abstract [en]

    In this paper, we present a visual servoing method based on a learned mapping between feature space and control space. Using a suitable recognition algorithm, we present and evaluate a complete method that simultaneously learns the appearance and control of a low-cost robotic arm. The recognition part is trained using an action precedes perception approach. The novelty of this paper, apart from the visual servoing method per se, is the combination of visual servoing with gripper recognition. We show that we can achieve high precision positioning without knowing in advance what the robotic arm looks like or how it is controlled.

    Keywords
    Gripper recognition; Jacobian estimation; LWPR; Visual servoing
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-21195 (URN)10.1016/j.imavis.2009.04.003 (DOI)
    Note
    Original Publication: Fredrik Larsson, Erik Jonsson and Michael Felsberg, Simultaneously learning to recognize and control a low-cost robotic arm, 2009, Image and Vision Computing, (27), 11, 1729-1739. http://dx.doi.org/10.1016/j.imavis.2009.04.003 Copyright: Elsevier Science B.V., Amsterdam. http://www.elsevier.com/ Available from: 2009-09-30 Created: 2009-09-30 Last updated: 2017-12-13Bibliographically approved
  • 212.
    Larsson, Fredrik
    Linköping University, Department of Electrical Engineering.
    Visual Servoing Based on Learned Inverse Kinematics2007Independent thesis Basic level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Initially an analytical closed-form inverse kinematics solution for a 5 DOF robotic arm was developed and implemented. This analytical solution proved not to meet the accuracy required for the shape sorting puzzle setup used in the COSPAL (COgnitiveSystems using Perception-Action Learning) project [2]. The correctness of the analytic model could be confirmed through a simulated ideal robot and the source of the problem was deemed to be nonlinearities introduced by weak servos unable to compensate for the effect of gravity. Instead of developing a new analytical model that took the effect of gravity into account, which would be erroneous when the characteristics of the robotic arm changed, e.g. when picking up a heavy object, a learning approach was selected.

    As learning method Locally Weighted Projection Regression (LWPR) [27] is used. It is an incremental supervised learning method and it is considered a state-ofthe-art method for function approximation in high dimensional spaces. LWPR is further combined with visual servoing. This allows for an improvement in accuracy by the use of visual feedback and the problems introduced by the weak servos can be solved. By combining the trained LWPR model with visual servoing, a high level of accuracy is reached, which is sufficient for the shape sorting puzzle setup used in COSPAL.

  • 213.
    Lef, Annette
    Linköping University, Department of Electrical Engineering, Computer Vision.
    CAD-Based Pose Estimation - Algorithm Investigation2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    One fundamental task in robotics is random bin-picking, where it is important to be able to detect an object in a bin and estimate its pose to plan the motion of a robotic arm. For this purpose, this thesis work aimed to investigate and evaluate algorithms for 6D pose estimation when the object was given by a CAD model. The scene was given by a point cloud illustrating a partial 3D view of the bin with multiple instances of the object. Two algorithms were thus implemented and evaluated. The first algorithm was an approach based on Point Pair Features, and the second was Fast Global Registration. For evaluation, four different CAD models were used to create synthetic data with ground truth annotations.

    It was concluded that the Point Pair Feature approach provided a robust localization of objects and can be used for bin-picking. The algorithm appears to be able to handle different types of objects, however, with small limitations when the object has flat surfaces and weak texture or many similar details. The disadvantage with the algorithm was the execution time. Fast Global Registration, on the other hand, did not provide a robust localization of objects and is thus not a good solution for bin-picking.

  • 214.
    Lemaire, Thomas
    et al.
    LAAS/CNRS 7, Toulouse, France.
    Berger, Cyrille
    LAAS/CNRS 7, Toulouse, France.
    Jung, Il-Kyun
    LAAS/CNRS 7, Toulouse, France.
    Lacroix, Simon
    LAAS/CNRS 7, Toulouse, France.
    Vision-Based SLAM: Stereo and Monocular Approaches2007In: International Journal of Computer Vision, ISSN 0920-5691, E-ISSN 1573-1405, Vol. 74, no 3, p. 343-364Article in journal (Refereed)
    Abstract [en]

    Building a spatially consistent model is a key functionality to endow a mobile robot with autonomy. Without an initial map or an absolute localization means, it requires to concurrently solve the localization and mapping problems. For this purpose, vision is a powerful sensor, because it provides data from which stable features can be extracted and matched as the robot moves. But it does not directly provide 3D information, which is a difficulty for estimating the geometry of the environment. This article presents two approaches to the SLAM problem using vision: one with stereovision, and one with monocular images. Both approaches rely on a robust interest point matching algorithm that works in very diverse environments. The stereovision based approach is a classic SLAM implementation, whereas the monocular approach introduces a new way to initialize landmarks. Both approaches are analyzed and compared with extensive experimental results, with a rover and a blimp.

  • 215.
    Lennartsson, Mattias
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Object Recognition with Cluster Matching2009Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Within this thesis an algorithm for object recognition called Cluster Matching has been developed, implemented and evaluated. The image information is sampled at arbitrary sample points, instead of interest points, and local image features are extracted. These sample points are used as a compact representation of the image data and can quickly be searched for prior known objects. The algorithm is evaluated on a test set of images and the result is surprisingly reliable and time efficient.

  • 216.
    Lenz, Reiner
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Generalized Pareto Distributions, Image Statistics and Autofocusing in Automated Microscopy2015In: GEOMETRIC SCIENCE OF INFORMATION, GSI 2015, Springer-Verlag New York, 2015, p. 96-103Conference paper (Refereed)
    Abstract [en]

    We introduce the generalized Pareto distributions as a statistical model to describe thresholded edge-magnitude image filter results. Compared to the more common Weibull or generalized extreme value distributions these distributions have at least two important advantages, the usage of the high threshold value assures that only the most important edge points enter the statistical analysis and the estimation is computationally more efficient since a much smaller number of data points have to be processed. The generalized Pareto distributions with a common threshold zero form a two-dimensional Riemann manifold with the metric given by the Fisher information matrix. We compute the Fisher matrix for shape parameters greater than -0.5 and show that the determinant of its inverse is a product of a polynomial in the shape parameter and the squared scale parameter. We apply this result by using the determinant as a sharpness function in an autofocus algorithm. We test the method on a large database of microscopy images with given ground truth focus results. We found that for a vast majority of the focus sequences the results are in the correct focal range. Cases where the algorithm fails are specimen with too few objects and sequences where contributions from different layers result in a multi-modal sharpness curve. Using the geometry of the manifold of generalized Pareto distributions more efficient autofocus algorithms can be constructed but these optimizations are not included here.

  • 217.
    Lenz, Reiner
    Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology.
    Octahedral Filters for 3D Image Processing2009In: Proceedings SSBA 2009, 2009, p. 109-112Conference paper (Other academic)
  • 218.
    Lenz, Reiner
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Positive Signal Spaces and the Mehler-Fock Transform2017In: GEOMETRIC SCIENCE OF INFORMATION, GSI 2017, SPRINGER INTERNATIONAL PUBLISHING AG , 2017, Vol. 10589, p. 745-753Conference paper (Refereed)
    Abstract [en]

    Eigenvector expansions and perspective projections are used to decompose a space of positive functions into a product of a half-axis and a solid unit ball. This is then used to construct a conical coordinate system where one component measures the distance to the origin, a radial measure of the distance to the axis and a unit vector describing the position on the surface of the ball. A Lorentz group is selected as symmetry group of the unit ball which leads to the Mehler-Fock transform as the Fourier transform of functions depending an the radial coordinate only. The theoretical results are used to study statistical properties of edge magnitudes computed from databases of image patches. The constructed radial values are independent of the orientation of the incoming light distribution (since edge-magnitudes are used), they are independent of global intensity changes (because of the perspective projection) and they characterize the second order statistical moment properties of the image patches. Using a large database of images of natural scenes it is shown that the generalized extreme value distribution provides a good statistical model of the radial components. Finally, the visual properties of textures are characterized using the Mehler-Fock transform of the probability density function of the generalized extreme value distribution.

  • 219.
    Lenz, Reiner
    et al.
    Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology.
    Oshima, Satoshi
    Chuo-University Tokyo Japan.
    Mochizuki, Rika
    Chuo-University Tokyo Japan.
    Chao, Jinhui
    Chuo-University Tokyo Japan.
    An Invariant Metric on the Manifold of Second Order Moments2009In: IEEE Color and Reflectance in Imaging and Computer Vision Workshop 2009 - CRICV 2009, IEEE-Computer Society , 2009, p. 1923-1930Conference paper (Refereed)
    Abstract [en]

    We introduce an invariant metric in the space of symmetric,positive definite matrices and illustrate the usage of thisspace together with this metric in color processing. For thismetric closed-form expressions for the distances and thegeodesics, (ie. the straight lines in this metric) are availableand we show how to implement them in the case of matricesof size 2x2. In the first illustration we use the framework toinvestigate an interpolation problem related to the ellipsesobtained in the measurements of just-noticeable-distances.For two such ellipses we use the metric to construct an interpolatingsequence of ellipses between them. In the secondapplication construct a texture descriptor for chromaticitydistributions. We describe the probability distributions ofchromaticity vectors by their matrices of second order moments.The distance between these matrices is independentunder linear changes of the coordinate system in the chromaticityspace and can therefore be used to define a distancebetween probability distributions that is independentof the coordinate system used. We illustrate this invariance,by way of an example, in the case of different white pointcorrections.

  • 220.
    Lenz, Reiner
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Zografos, Vasileios
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Fisher Information and the Combination of RGB channels2013In: 4th International Workshop, CCIW 2013, Chiba, Japan, March 3-5, 2013. Proceedings / [ed] Shoji Tominaga, Raimondo Schettini, Alain Trémeau, Springer Berlin/Heidelberg, 2013, p. 250-264Conference paper (Refereed)
    Abstract [en]

    We introduce a method to combine the color channels of an image to a scalar valued image. Linear combinations of the RGB channels are constructed using the Fisher-Trace-Information (FTI), defined as the trace of the Fisher information matrix of the Weibull distribution, as a cost function. The FTI characterizes the local geometry of the Weibull manifold independent of the parametrization of the distribution. We show that minimizing the FTI leads to contrast enhanced images, suitable for segmentation processes. The Riemann structure of the manifold of Weibull distributions is used to design optimization methods for finding optimal weight RGB vectors. Using a threshold procedure we find good solutions even for images with limited content variation. Experiments show how the method adapts to images with widely varying visual content. Using these image dependent de-colorizations one can obtain substantially improved segmentation results compared to a mapping with pre-defined coefficients.

  • 221.
    Lesmana, Martin
    et al.
    Computer Science, University of British Columbia, Canada.
    Landgren, Axel
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Forssén, Per-Erik
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Pai, Dinesh K.
    Computer Science, University of British Columbia, Canada.
    Active Gaze Stabilization2014In: Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing / [ed] A. G. Ramakrishnan, ACM Digital Library, 2014, p. 81:1-81:8Conference paper (Refereed)
    Abstract [en]

    We describe a system for active stabilization of cameras mounted on highly dynamic robots. To focus on careful performance evaluation of the stabilization algorithm, we use a camera mounted on a robotic test platform that can have unknown perturbations in the horizontal plane, a commonly occurring scenario in mobile robotics. We show that the camera can be eectively stabilized using an inertial sensor and a single additional motor, without a joint position sensor. The algorithm uses an adaptive controller based on a model of the vertebrate Cerebellum for velocity stabilization, with additional drift correction. We have alsodeveloped a resolution adaptive retinal slip algorithm that is robust to motion blur.

    We evaluated the performance quantitatively using another high speed robot to generate repeatable sequences of large and fast movements that a gaze stabilization system can attempt to counteract. Thanks to the high-accuracy repeatability, we can make a fair comparison of algorithms for gaze stabilization. We show that the resulting system can reduce camera image motion to about one pixel per frame on average even when the platform is rotated at 200 degrees per second. As a practical application, we also demonstrate how the common task of face detection benets from active gaze stabilization.

  • 222.
    Lind, Johan
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Make it Meaningful: Semantic Segmentation of Three-Dimensional Urban Scene Models2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Semantic segmentation of a scene aims to give meaning to the scene by dividing it into meaningful — semantic — parts. Understanding the scene is of great interest for all kinds of autonomous systems, but manual annotation is simply too time consuming, which is why there is a need for an alternative approach. This thesis investigates the possibility of automatically segmenting 3D-models of urban scenes, such as buildings, into a predetermined set of labels. The approach was to first acquire ground truth data by manually annotating five 3D-models of different urban scenes. The next step was to extract features from the 3D-models and evaluate which ones constitutes a suitable feature space. Finally, three supervised learners were implemented and evaluated: k-Nearest Neighbour (KNN), Support Vector Machine (SVM) and Random Classification Forest (RCF). The classifications were done point-wise, classifying each 3D-point in the dense point cloud belonging to the model being classified.

    The result showed that the best suitable feature space is not necessarily the one containing all features. The KNN classifier got the highest average accuracy overall models — classifying 42.5% of the 3D points correct. The RCF classifier managed to classify 66.7% points correct in one of the models, but had worse performance for the rest of the models and thus resulting in a lower average accuracy compared to KNN. In general, KNN, SVM, and RCF seemed to have different benefits and drawbacks. KNN is simple and intuitive but by far the slowest classifier when dealing with a large set of training data. SVM and RCF are both fast but difficult to tune as there are more parameters to adjust. Whether the reason for obtaining the relatively low highest accuracy was due to the lack of ground truth training data, unbalanced validation models, or the capacity of the learners, was never investigated due to a limited time span. However, this ought to be investigated in future studies.

  • 223.
    Lindahl, Tobias
    Linköping University, Department of Science and Technology.
    Study of Local Binary Patterns2007Independent thesis Advanced level (degree of Magister), 20 points / 30 hpStudent thesis
    Abstract [en]

    This Masters thesis studies the concept of local binary patterns, which describe the neighbourhood of a pixel in a digital image by binary derivatives. The operator is often used in texture analysis and has been successfully used in facial recognition.

    This thesis suggests two methods based on some basic ideas of Björn Kruse and studies of literature on the subject. The first suggested method presented is an algorithm which reproduces images from their local binary patterns by a kind of integration of the binary derivatives. This method is a way to prove the preservation of information. The second suggested method is a technique of interpolating missing pixels in a single CCD camera based on local binary patterns and machine learning. The algorithm has shown some very promising results even though in its current form it does not keep up with the best algorithms of today.

  • 224.
    Linderhed, Anna
    et al.
    FOI.
    Wadströmer, Niclas
    FOI.
    Stenborg, Karl-Göran
    FOI.
    Nautsch, Harald
    Linköping University, Department of Electrical Engineering, Image Coding. Linköping University, The Institute of Technology.
    Compression of Hyperspectral data for Automated Analysis2009In: SPIE Europe Remote Sensing 2009, 2009Conference paper (Other academic)
    Abstract [en]

    State of the art and coming hyperspectral optical sensors generate large amounts of data and automatic analysis is necessary. One example is Automatic Target Recognition (ATR), frequently used in military applications and a coming technique for civilian surveillance applications. When sensors communicate in networks, the capacity of the communication channel defines the limit of data transferred without compression. Automated analysis may have different demands on data quality than a human observer, and thus standard compression methods may not be optimal. This paper presents results from testing how the performance of detection methods are affected by compressing input data with COTS coders. A standard video coder has been used to compress hyperspectral data. A video is a sequence of still images, a hybrid video coder use the correlation in time by doing block based motion compensated prediction between images. In principle only the differences are transmitted. This method of coding can be used on hyperspectral data if we consider one of the three dimensions as the time axis. Spectral anomaly detection is used as detection method on mine data. This method finds every pixel in the image that is abnormal, an anomaly compared to the surroundings. The purpose of anomaly detection is to identify objects (samples, pixels) that differ significantly from the background, without any a priori explicit knowledge about the signature of the sought-after targets. Thus the role of the anomaly detector is to identify “hot spots” on which subsequent analysis can be performed. We have used data from Imspec, a hyperspectral sensor. The hyperspectral image, or the spectral cube, consists of consecutive frames of spatial-spectral images. Each pixel contains a spectrum with 240 measure points. Hyperspectral sensor data was coded with hybrid coding using a variant of MPEG2. Only I- and P- frames was used. Every 10th frame was coded as P frame. 14 hyperspectral images was coded in 3 different directions using x, y, or z direction as time. 4 different quantization steps were used. Coding was done with and without initial quantization of data to 8 bbp. Results are presented from applying spectral anomaly detection on the coded data set.

     

  • 225.
    Lindholm, Stefan
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Bock, Alexander
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Poor Man’s Rendering Of Segmented Data2013In: Proceedings of SIGRAD 2013; Visual Computing, June 13-14; 2013, Norrköping, Sweden / [ed] Timo Ropinski and Jonas Unger, Linköping: Linköping University Electronic Press, 2013, p. 49-54Conference paper (Refereed)
    Abstract [en]

    In this paper we present a set of techniques for fast and efficient rendering of segmented data. Our approach utilizes the expected difference between two co-located texture lookups of a label volume, taken with different interpolation filters, as a feature boundary indicator. This allows us to achieve smooth class boundaries without needing to explicitly sample all eight neighbors in the label volume as is the case with previous methods. We also present a data encoding scheme that greatly simplifies transfer function construction.

  • 226.
    Liu, Jin
    et al.
    School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangshu, China; School of Engineering and Information Technology, The University of New South Wales, Canberra, Australia.
    Pham, Tuan D
    School of Engineering and Information Technology, The University of New South Wales, Canberra, Australia.
    A spatially constrained fuzzy hyper-prototype clustering algorithm2012In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 45, no 4, p. 1759-1771Article in journal (Refereed)
    Abstract [en]

    We present in this paper a fuzzy clustering algorithm which can handle spatially constraint problems often encountered in pattern recognition. The proposed method is based on the notions of hyperplanes, the fuzzy c-means, and spatial constraints. By adding a spatial regularizer into the fuzzy hyperplane-based objective function, the proposed method can take into account additionally important information of inherently spatial data. Experimental results have demonstrated that the proposed algorithm achieves superior results to some other popular fuzzy clustering models, and has potential for cluster analysis in spatial domain.

  • 227.
    Liu, Jin
    et al.
    School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia.
    Pham, Tuan D
    School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia.
    FHC: The fuzzy hyper-prototype clustering algorithm2012In: Journal of Knowledge-based & Intelligent Engineering Systems, ISSN 1327-2314, E-ISSN 1875-8827, Vol. 16, no 1, p. 35-47Article in journal (Refereed)
    Abstract [en]

    We propose a fuzzy hyper-prototype clustering algorithm in this paper. This approach uses hyperplanes to represent the cluster centers in the fuzzy clustering. We present the formulation of fuzzy objective function and derive an iterative numerical algorithm for minimizing the objective function. Validations and comparisons are made between the proposed fuzzy clustering algorithm and existing fuzzy clustering methods on artificially generated data as well as on real world dataset include UCI dataset and gene expression dataset, the results show that the proposed method can give better performance in the above cases.

  • 228.
    Liu, Jin
    et al.
    School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, 2600, Australia .
    Pham, Tuan D
    School of Engineering and Information Technology University of New South Wales Canberra, ACT 2600, Australia.
    Fuzzy hyper-prototype clustering2010In: Knowledge-Based and Intelligent Information and Engineering Systems: 14th International Conference, KES 2010, Cardiff, UK, September 8-10, 2010, Proceedings, Part I / [ed] Rossitza Setchi, Ivan Jordanov, Robert J. Howlett, Lakhmi C. Jain, Springer Berlin/Heidelberg, 2010, 6276, p. 379-389Chapter in book (Other academic)
    Abstract [en]

    We propose a fuzzy hyper-prototype algorithm in this paper. This approach uses hyperplanes to represent the cluster centers in the fuzzy c-means algorithm. We present the formulation of a hyperplanebased fuzzy objective function and then derive an iterative numerical procedure for minimizing the clustering criterion. We tested the method with data degraded with random noise. The experimental results show that the proposed method is robust to clustering noisy linear structure.

  • 229.
    Liu, Yang
    et al.
    Vaasa University of Applied Sciences, Vaasa, Finland .
    Liu, Dong
    Vaasa University of Applied Sciences, Vaasa, Finland .
    Movement Status Based Vision Filter for RoboCup Small-Size League2012In: Advances in Automation and Robotics, Vol. 2: Selected Papers from the 2011 International Conference on Automation and Robotics (ICAR 2011), Dubai, December 1–2, 2011 / [ed] Gary Lee, Springer, 2012, p. 79-86Chapter in book (Other academic)
    Abstract [en]

    Small-size soccer league is a division of the RoboCup (Robot world cup) competitions. Each team uses its own designed hardware and software to compete with othersunder defined rules. There are two kinds of data which the strategy system will receive from the dedicated server, one of them is the referee commands, and the other one is vision data. However, due to the network delay and the vision noise, we have to process the data before we can actually use it. Therefore, a certain mechanism is needed in this case.Instead of using some prevalent and complex algorithms, this paper proposes to solve this problem from simple kinematics and mathematics point of view, which can be implemented effectively by hobbyists and undergraduate students. We divide this problem by the speed status and deal it in three different situations. Testing results show good performance with this algorithm and great potential in filtering vision data thus forecasting actual coordinates of tracking objects.

  • 230.
    Ljungström, Carl
    Linköping University, Department of Electrical Engineering, Image Coding.
    Design and Implementation of an Analog Video Signal Quality Measuring Software for Component Video2010Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    An IP based set-top box (STB) is essentially a lightweight computer used to receive video over the Internet and convert it to analog or digital signals understood by the television. During this transformation from a digital image to an analog video signal many different types of distortions can occur. Some of these distortions will affect the image quality in a negative way. If these distortions could be measured they might be corrected and give the system a better image quality.

    This thesis is a continuation of two previous theses where a custom hardware for sampling analog component video signals was created. A software used to communicatewith the sampling hardware and perform several different measurementson the samples collected has been created in this thesis.

    The analog video signal quality measurement system has been compared to a similar commercial product and it was found that all except two measurement methods gave very good results. The remaining two measurement methods gave acceptable result. However the differences might be due to differences in implementation. The most important thing for the measurement system is to have consistency. If a system has consistency then any changes leading to worse videoquality can be found.

  • 231.
    Lundagårds, Marcus
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Vehicle Detection in Monochrome Images2008Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The purpose of this master thesis was to study computer vision algorithms for vehicle detection in monochrome images captured by mono camera. The work has mainly been focused on detecting rear-view cars in daylight conditions. Previous work in the literature have been revised and algorithms based on edges, shadows and motion as vehicle cues have been modified, implemented and evaluated. This work presents a combination of a multiscale edge based detection and a shadow based detection as the most promising algorithm, with a positive detection rate of 96.4% on vehicles at a distance of between 5 m to 30 m. For the algorithm to work in a complete system for vehicle detection, future work should be focused on developing a vehicle classifier to reject false detections.

  • 232.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Technical report: Measuring digital image quality2006Report (Other academic)
    Abstract [en]

    Imaging is an invaluable tool in many research areas and other advanced domains such as health care. When developing any system dealing with images, image quality issues are insurmountable. This report describes digital image quality from many viewpoints, from retinal receptor characteristics to perceptual compression algorithms. Special focus is given to perceptual image quality measures.

  • 233.
    Lundström, Claes
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ljung, Patric
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Systems for visualizing images using explicit quality prioritization of a feature (s) in multidimensional image data sets, related methods and computer products2010Patent (Other (popular science, discussion, etc.))
    Abstract [en]

    Visualization systems for rendering images from a multi-dimensional data set, include an interactive visualization system configured to accept user input to define at least one explicit prioritized feature in an image rendered from a multi-dimensional image data set. The at least one prioritized feature is automatically electronically rendered with high or full quality in different interactively requested rendered images of the image data while other non-prioritized features are rendered at lower quality. The visualization system may optionally include a rendering system configured to render images by electronically assigning a level of detail for different tiles associated with an image, each level of detail having a number of pixel samples to be calculated to thereby accelerate image processing.

  • 234.
    Lundström, Dennis
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Data-efficient Transfer Learning with Pre-trained Networks2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Deep learning has dominated the computer vision field since 2012, but a common criticism of deep learning methods is their dependence on large amounts of data. To combat this criticism research into data-efficient deep learning is growing. The foremost success in data-efficient deep learning is transfer learning with networks pre-trained on the ImageNet dataset. Pre-trained networks have achieved state-of-the-art performance on many tasks. We consider the pre-trained network method for a new task where we have to collect the data. We hypothesize that the data efficiency of pre-trained networks can be improved through informed data collection. After exhaustive experiments on CaffeNet and VGG16, we conclude that the data efficiency indeed can be improved. Furthermore, we investigate an alternative approach to data-efficient learning, namely adding domain knowledge in the form of a spatial transformer to the pre-trained networks. We find that spatial transformers are difficult to train and seem to not improve data efficiency.

  • 235.
    Läthén, Gunnar
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology.
    Segmentation Methods for Medical Image Analysis: Blood vessels, multi-scale filtering and level set methods2010Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Image segmentation is the problem of partitioning an image into meaningful parts, often consisting of an object and background. As an important part of many imaging applications, e.g. face recognition, tracking of moving cars and people etc, it is of general interest to design robust and fast segmentation algorithms. However, it is well accepted that there is no general method for solving all segmentation problems. Instead, the algorithms have to be highly adapted to the application in order to achieve good performance. In this thesis, we will study segmentation methods for blood vessels in medical images. The need for accurate segmentation tools in medical applications is driven by the increased capacity of the imaging devices. Common modalities such as CT and MRI generate images which simply cannot be examined manually, due to high resolutions and a large number of image slices. Furthermore, it is very difficult to visualize complex structures in three-dimensional image volumes without cutting away large portions of, perhaps important, data. Tools, such as segmentation, can aid the medical staff in browsing through such large images by highlighting objects of particular importance. In addition, segmentation in particular can output models of organs, tumors, and other structures for further analysis, quantification or simulation.

    We have divided the segmentation of blood vessels into two parts. First, we model the vessels as a collection of lines and edges (linear structures) and use filtering techniques to detect such structures in an image. Second, the output from this filtering is used as input for segmentation tools. Our contributions mainly lie in the design of a multi-scale filtering and integration scheme for de- tecting vessels of varying widths and the modification of optimization schemes for finding better segmentations than traditional methods do. We validate our ideas on synthetical images mimicking typical blood vessel structures, and show proof-of-concept results on real medical images.

    List of papers
    1. Flexible and Topologically Localized Segmentation
    Open this publication in new window or tab >>Flexible and Topologically Localized Segmentation
    2007 (English)In: EuroVis07 Joint Eurographics: IEEE VGTC Symposium on Visualization / [ed] Ken Museth, Torsten Möller, and Anders Ynnerman, Aire-la-Ville, Switzerland: Eurographics Association , 2007, , p. 179-186p. 179-186Conference paper, Published paper (Refereed)
    Abstract [en]

    One of the most common visualization tasks is the extraction of significant boundaries, often performed with iso- surfaces or level set segmentation. Isosurface extraction is simple and can be guided by geometric and topological analysis, yet frequently does not extract the desired boundary. Level set segmentation is better at boundary extrac- tion, but either leads to global segmentation without edges, [CV01], that scales unfavorably in 3D or requires an initial estimate of the boundary from which to locally solve segmentation with edges. We propose a hybrid system in which topological analysis is used for semi-automatic initialization of a level set segmentation, and geometric information bounded topologically is used to guide and accelerate an iterative segmentation algorithm that com- bines several state-of-the-art level set terms. We thus combine and improve both the flexible isosurface interface and level set segmentation without edges.

    Place, publisher, year, edition, pages
    Aire-la-Ville, Switzerland: Eurographics Association, 2007. p. 179-186
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-40841 (URN)54293 (Local ID)978-3-905673-45-6 (ISBN)54293 (Archive number)54293 (OAI)
    Conference
    Eurographics/ IEEE-VGTC Symposium on Visualization, 23-25 May, Norrköping, Sweden
    Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2013-09-19
    2. Phase Based Level Set Segmentation of Blood Vessels
    Open this publication in new window or tab >>Phase Based Level Set Segmentation of Blood Vessels
    2008 (English)In: Proceedings of 19th International Conference on Pattern Recognition, IEEE Computer Society , 2008, p. 1-4Conference paper, Published paper (Refereed)
    Abstract [en]

    The segmentation and analysis of blood vessels hasreceived much attention in the research community. Theresults aid numerous applications for diagnosis andtreatment of vascular diseases. Here we use level setpropagation with local phase information to capture theboundaries of vessels. The basic notion is that localphase, extracted using quadrature filters, allows us todistinguish between lines and edges in an image. Notingthat vessels appear either as lines or edge pairs, weintegrate multiple scales and capture information aboutvessels of varying width. The outcome is a “global”phase which can be used to drive a contour robustly towardsthe vessel edges. We show promising results in2D and 3D. Comparison with a related method givessimilar or even better results and at a computationalcost several orders of magnitude less. Even with verysparse initializations, our method captures a large portionof the vessel tree.

    Place, publisher, year, edition, pages
    IEEE Computer Society, 2008
    Series
    International Conference on Pattern Recognition, ISSN 1051-4651
    National Category
    Medical Laboratory and Measurements Technologies
    Identifiers
    urn:nbn:se:liu:diva-21054 (URN)10.1109/ICPR.2008.4760970 (DOI)000264729000023 ()978-1-4244-2175-6 (ISBN)978-1-4244-2174-9 (ISBN)
    Conference
    19th International Conference on Pattern Recognition (ICPR 2008), 8-11 December 2008, Tampa, Finland
    Note

    ©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE: Gunnar Läthén, Jimmy Jonasson and Magnus Borga, Phase Based Level Set Segmentation of Blood Vessels, 2008, Proceedings of 19th International Conference on Pattern Recognition. http://dx.doi.org/10.1109/ICPR.2008.4760970

    Available from: 2009-09-28 Created: 2009-09-28 Last updated: 2015-10-09
    3. Momentum Based Optimization Methods for Level Set Segmentation
    Open this publication in new window or tab >>Momentum Based Optimization Methods for Level Set Segmentation
    2009 (English)In: Momentum Based Optimization Methods for Level Set Segmentation: Second International Conference, SSVM 2009, Voss, Norway, June 1-5, 2009. Proceedings / [ed] Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen, Berlin: Springer Berlin/Heidelberg, 2009, p. 124-136Conference paper, Published paper (Refereed)
    Abstract [en]

    Segmentation of images is often posed as a variational problem. As such, it is solved by formulating an energy functional depending on a contour and other image derived terms. The solution of the segmentation problem is the contour which extremizes this functional. The standard way of solving this optimization problem is by gradient descent search in the solution space, which typically suffers from many unwanted local optima and poor convergence. Classically, these problems have been circumvented by modifying the energy functional. In contrast, the focus of this paper is on alternative methods for optimization. Inspired by ideas from the machine learning community, we propose segmentation based on gradient descent with momentum. Our results show that typical models hampered by local optima solutions can be further improved by this approach. We illustrate the performance improvements using the level set framework.

    Place, publisher, year, edition, pages
    Berlin: Springer Berlin/Heidelberg, 2009
    Series
    Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 5567
    National Category
    Medical Laboratory and Measurements Technologies
    Identifiers
    urn:nbn:se:liu:diva-21037 (URN)10.1007/978-3-642-02256-2_11 (DOI)000270543900011 ()3-642-02255-3 (ISBN)978-3-642-02255-5 (ISBN)978-3-642-02256-2 (ISBN)
    Conference
    Second International Conference, SSVM 2009, June 1-5, Voss, Norway
    Note

    Original Publication: Gunnar Läthén, Thord Andersson, Reiner Lenz and Magnus Borga, Momentum Based Optimization Methods for Level Set Segmentation, 2009, Lecture Notes in Computer Science 5567: Scale Space and Variational Methods in Computer Vision, 124-136. http://dx.doi.org/10.1007/978-3-642-02256-2_11 Copyright: Springer http://www.springerlink.com/

    Available from: 2009-09-28 Created: 2009-09-28 Last updated: 2018-02-19Bibliographically approved
    4. A Fast Optimization Method for Level Set Segmentation
    Open this publication in new window or tab >>A Fast Optimization Method for Level Set Segmentation
    2009 (English)In: Image Analysis: 16th Scandinavian Conference, SCIA 2009, Oslo, Norway, June 15-18, 2009. Proceedings / [ed] A.-B. Salberg, J.Y. Hardeberg, and R. Jenssen, Springer Berlin/Heidelberg, 2009, p. 400-409Conference paper, Published paper (Refereed)
    Abstract [en]

    Level set methods are a popular way to solve the image segmentation problem in computer image analysis. A contour is implicitly represented by the zero level of a signed distance function, and evolved according to a motion equation in order to minimize a cost function. This function defines the objective of the segmentation problem and also includes regularization constraints. Gradient descent search is the de facto method used to solve this optimization problem. Basic gradient descent methods, however, are sensitive for local optima and often display slow convergence. Traditionally, the cost functions have been modified to avoid these problems. In this work, we instead propose using a modified gradient descent search based on resilient propagation (Rprop), a method commonly used in the machine learning community. Our results show faster convergence and less sensitivity to local optima, compared to traditional gradient descent.

    Place, publisher, year, edition, pages
    Springer Berlin/Heidelberg, 2009
    Series
    Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 5575
    Keywords
    Image segmentation - level set method - optimization - gradient descent - Rprop - variational problems - active contours
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-19313 (URN)10.1007/978-3-642-02230-2_41 (DOI)000268661000041 ()978-3-642-02229-6 (ISBN)978-3-642-02230-2 (ISBN)
    Conference
    16th Scandinavian Conference on Image Analysis, June 15-18 2009, Oslo, Norway
    Available from: 2009-07-09 Created: 2009-06-17 Last updated: 2018-01-23Bibliographically approved
    5. Blood vessel segmentation using multi-scale quadrature filtering
    Open this publication in new window or tab >>Blood vessel segmentation using multi-scale quadrature filtering
    2010 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 31, no 8, p. 762-767Article in journal (Refereed) Published
    Abstract [en]

    The segmentation of blood vessels is a common problem in medical imagingand various applications are found in diagnostics, surgical planning, trainingand more. Among many dierent techniques, the use of multiple scales andline detectors is a popular approach. However, the typical line lters usedare sensitive to intensity variations and do not target the detection of vesselwalls explicitly. In this article, we combine both line and edge detection usingquadrature lters across multiple scales. The lter result gives well denedvessels as linear structures, while distinct edges facilitate a robust segmentation.We apply the lter output to energy optimization techniques for segmentationand show promising results in 2D and 3D to illustrate the behavior of ourmethod. The conference version of this article received the best paper award inthe bioinformatics and biomedical applications track at ICPR 2008.

    Place, publisher, year, edition, pages
    Elsevier, 2010
    Keywords
    Image segmentation, Blood vessels, Medical imaging, Multi-scale, Quadrature filter, Level set method
    National Category
    Medical Laboratory and Measurements Technologies
    Identifiers
    urn:nbn:se:liu:diva-21046 (URN)10.1016/j.patrec.2009.09.020 (DOI)000277552600014 ()
    Note
    Original Publication: Gunnar Läthén, Jimmy Jonasson and Magnus Borga, Blood vessel segmentation using multi-scale quadrature filtering, 2010, Pattern Recognition Letters, (31), 8, 762-767. http://dx.doi.org/10.1016/j.patrec.2009.09.020 Copyright: Elsevier Science B.V., Amsterdam. http://www.elsevier.com/ Available from: 2009-09-28 Created: 2009-09-28 Last updated: 2017-12-13
  • 236.
    Läthén, Gunnar
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Cros, Olivier
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Non-ring Filters for Robust Detection of Linear Structures2010In: Proceedings of the 20th International Conference on Pattern Recognition, Los Alamitos, CA, USA: IEEE Computer Society, 2010, p. 233-236Conference paper (Refereed)
    Abstract [en]

    Many applications in image analysis include the problem of linear structure detection, e.g. segmentation of blood vessels in medical images, roads in satellite images, etc. A simple and efficient solution is to apply linear filters tuned to the structures of interest and extract line and edge positions from the filter output. However, if the filter is not carefully designed, artifacts such as ringing can distort the results and hinder a robust detection. In this paper, we study the ringing effects using a common Gabor filter for linear structure detection, and suggest a method for generating non-ring filters in 2D and 3D. The benefits of the non-ring design are motivated by results on both synthetic and natural images.

  • 237.
    Löfgren, Fredrik
    et al.
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Dybeck, Jon
    Linköping University, University Services.
    Heintz, Fredrik
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Qualification document: RoboCup 2015 Standard Platform League2015Conference paper (Other academic)
    Abstract [en]

    This is the application for the RoboCup 2015 StandardPlatform League from the ”LiU Robotics” team. In thisdocument we present ourselves and what we want to achieve byour participation in the conference and competition

  • 238.
    Löw, Joakim
    et al.
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Eldén, Lars
    Linköping University, Department of Mathematics, Scientific Computing. Linköping University, The Institute of Technology.
    Numerical Analysis of BRDFs for Inverse Rendering2009Report (Other academic)
    Abstract [en]

    The properties of materials which are present in a scene determine how geometry reflects and distributes light in the scene. This text presents work-in-progress on numerical analysis of bidirectional reflection distribution functions (BRDF) corresponding to various materials, with a focus on inverse rendering. An analysis of these functions is vital for the understanding of the behaviour of reflected light under different lighting conditions, and in the application of inverse rendering, it is important in order to determine what quality one can expect from recovered data. We discuss the singular value decompositions of a few materials, their effect on the ill-posedness of the inverse problem related to the reflectance equation and how regularization affects the solution of the problem.

  • 239.
    Löw, Joakim
    et al.
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Larsson, Per
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Unger, Jonas
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    HDR Light Probe Sequence Resampling for Realtime Incident Light Field Rendering2009In: Proceedings - SCCG 2009: 25th Spring Conference on Computer Graphics / [ed] Helwig Hauser, New York, USA: ACM New York , 2009, p. 43-50Conference paper (Refereed)
    Abstract [en]

    This paper presents a method for resampling a sequence of high dynamic range light probe images into a representation of Incident Light Field (ILF) illumination which enables realtime rendering. The light probe sequences are captured at varying positions in a real world environment using a high dynamic range video camera pointed at a mirror sphere. The sequences are then resampled to a set of radiance maps in a regular three dimensional grid before projection onto spherical harmonics. The capture locations and amount of samples in the original data make it inconvenient for direct use in rendering and resampling is necessary to produce an efficient data structure. Each light probe represents a large set of incident radiance samples from different directions around the capture location. Under the assumption that the spatial volume in which the capture was performed has no internal occlusion, the radiance samples are projected through the volume along their corresponding direction in order to build a new set of radiance maps at selected locations, in this case a three dimensional grid. The resampled data is projected onto a spherical harmonic basis to allow for realtime lighting of synthetic objects inside the incident light field.

  • 240.
    M. Fard, Farhad
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Quantitative image based modelling of food on aplate2012Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The main purpose of this work is to reconstruct 3D model of an entire scene byusing two ordinary cameras. We develop a mobile phone application, based onstereo vision and image analysis algorithms, executed either locally or on a remotehost, to calculate the dietary intake using the current questionnaire and the mobilephone photographs. The information of segmented 3D models are used to calculatethe volume -and then the calories- of a person’s daily intake food. The method ischecked using different solid food samples, in different camera arrangements. Theresults shows that the method successfully reconstructs 3D model of different foodsample with high details.

  • 241.
    Magnusson, Filip
    Linköping University, Department of Computer and Information Science, Software and Systems.
    Evaluating Deep Learning Algorithms for Steering an Autonomous Vehicle2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    With self-driving cars on the horizon, vehicle autonomy and its problems is a hot topic. In this study we are using convolutional neural networks to make a robot car avoid obstacles. The robot car has a monocular camera, and our approach is to use the images taken by the camera as input, and then output a steering command. Using this method the car is to avoid any object in front of it.

    In order to lower the amount of training data we use models that are pretrained on ImageNet, a large image database containing millions of images. The model are then trained on our own dataset, which contains of images taken directly by the robot car while driving around. The images are then labeled with the steering command used while taking the image. While training we experiment with using different amounts of frozen layers. A frozen layer is a layer that has been pretrained on ImageNet, but are not trained on our dataset.

    The Xception, MobileNet and VGG16 architectures are tested and compared to each other.

    We find that a lower amount of frozen layer produces better results, and our best model, which used the Xception architecture, achieved 81.19% accuracy on our test set. During a qualitative test the car avoid collisions 78.57% of the time.

  • 242. Marconi, L.
    et al.
    Melchiorri, C.
    Beetz, M.
    Pangercic, D.
    Siegwart, R.
    Leutenegger, S.
    Carloni, R.
    Stramigioli, S.
    Bruyninckx, H.
    Doherty, Patrick
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, The Institute of Technology.
    Kleiner, Alexander
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, The Institute of Technology.
    Lippiello, V.
    Finzi, A.
    Siciliano, B.
    Sala, A.
    Tomatis, N.
    The SHERPA project: Smart collaboration between humans and ground-aerial robots for improving rescuing activities in alpine environments2012In: Proc. of the IEEE Int. Workshop on Safety, Security and Rescue Robotics (SSRR), IEEE , 2012, p. 1-4Conference paper (Refereed)
    Abstract [en]

    The goal of the paper is to present the foreseen research activity of the European project “SHERPA” whose activities will start officially on February 1th 2013. The goal of SHERPA is to develop a mixed ground and aerial robotic platform to support search and rescue activities in a real-world hostile environment, like the alpine scenario that is specifically targeted in the project. Looking into the technological platform and the alpine rescuing scenario, we plan to address a number of research topics about cognition and control. What makes the project potentially very rich from a scientific viewpoint is the heterogeneity and the capabilities to be owned by the different actors of the SHERPA system: the human rescuer is the “busy genius”, working in team with the ground vehicle, as the “intelligent donkey”, and with the aerial platforms, i.e. the “trained wasps” and “patrolling hawks”. Indeed, the research activity focuses on how the “busy genius” and the “SHERPA animals” interact and collaborate with each other, with their own features and capabilities, toward the achievement of a common goal.

  • 243.
    Maria Marreiros, Filipe Miguel
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Rossitti, Sandro
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Anaesthetics, Operations and Specialty Surgery Center, Department of Neurosurgery.
    Gustafsson, Torbjörn
    XM Reality Research AB, Linköping, Sweden.
    Carleberg, Per
    XM Reality Research AB, Linköping, Sweden.
    Smedby, Örjan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Multi-view 3D vessel tracking using near-infrared cameras2014In: Proceedings of the 27th International Congress and Exhibition on Computer Assisted Radiology and Surgery: Image Processing and Visualization, Springer, 2014, p. S165-S165Conference paper (Other academic)
  • 244.
    Maria Marreiros, Filipe Miguel
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Rossitti, Sandro
    Östergötlands Läns Landsting, Anaesthetics, Operations and Specialty Surgery Center, Department of Neurosurgery. Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences.
    Karlsson, Per
    Östergötlands Läns Landsting, Anaesthetics, Operations and Specialty Surgery Center, Department of Neurosurgery.
    Wang, Chunliang
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Royal Institute of Technology, School of Technology and Health, Alfred Nobels Allé 10, Huddinge.
    Gustafsson, Torbjörn
    XM Reality AB, Linköping, Sweden.
    Carleberg, Per
    XM Reality AB, Linköping, Sweden.
    Smedby, Örjan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Royal Institute of Technology, School of Technology and Health, Alfred Nobels Allé 10, Huddinge .
    Superficial vessel reconstruction with a multiview camera system2016In: Journal of Medical Imaging, ISSN 2329-4302, E-ISSN 2329-4310, Vol. 3, no 1, p. 015001-1-015001-13Article in journal (Refereed)
    Abstract [en]

    We aim at reconstructing superficial vessels of the brain. Ultimately, they will serve to guide the deformationmethods to compensate for the brain shift. A pipeline for three-dimensional (3-D) vessel reconstructionusing three mono-complementary metal-oxide semiconductor cameras has been developed. Vessel centerlinesare manually selected in the images. Using the properties of the Hessian matrix, the centerline points areassigned direction information. For correspondence matching, a combination of methods was used. The processstarts with epipolar and spatial coherence constraints (geometrical constraints), followed by relaxation labelingand an iterative filtering where the 3-D points are compared to surfaces obtained using the thin-plate spline withdecreasing relaxation parameter. Finally, the points are shifted to their local centroid position. Evaluation invirtual, phantom, and experimental images, including intraoperative data from patient experiments, showsthat, with appropriate camera positions, the error estimates (root-mean square error and mean error) are∼1 mm.

  • 245.
    Maria Marreiros, Filipe Miguel
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Smedby, Örjan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences.
    GPU-based ray-casting of non-rigid deformations: a comparison between direct and indirect approaches2014In: Proceedings of SIGRAD 2014, Visual Computing, June 12-13, 2014, Göteborg, Sweden / [ed] Mohammad Obaid; Daniel Sjölie; Erik Sintorn; Morten Fjeld, Linköping University Electronic Press, 2014, p. 67-74Conference paper (Refereed)
    Abstract [en]

    For ray-casting of non-rigid deformations, the direct approach (as opposed to the traditional indirect approach) does not require the computation of an intermediate volume to be used for the rendering step. The aim of this study was to compare the two approaches in terms of performance (speed) and accuracy (image quality).

    The direct and the indirect approach were carefully implemented to benefit of the massive GPU parallel power, using CUDA. They were then tested with Computed Tomography (CT) datasets of varying sizes and with a synthetic image, the Marschner-Lobb function.

    The results show that the direct approach is dependent on the ray sampling steps, number of landmarks and image resolution. The indirect approach is mainly affected by the number of landmarks, if the volume is large enough.

    These results exclude extreme cases, i.e. if the sampling steps are much smaller than the voxel size and if the image resolution is much higher than the ones used here. For a volume of size 512×512×512, using 100 landmarks and image resolution of 1280×960, the direct method performs better if the ray sampling steps are approximately above 1 voxel. Regarding accuracy, the direct method provides better results for multiple frequencies using the Marschner-Lobb function.

    The conclusion is that the indirect method is superior in terms of performance, if the sampling along the rays is high, in comparison to the voxel grid, while the direct is superior otherwise. The accuracy analysis seems to point out that the direct method is superior, in particular when the implicit function is used.

  • 246.
    Maria Marreiros, Filipe Miguel
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Smedby, Örjan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Mirror Stereoscopic Display for Direct Volume Rendering2014In: Proceedings of SIGRAD 2014, Visual Computing, June 12-13, 2014, Göteborg, Sweden / [ed] Mohammad Obaid; Daniel Sjölie; Erik Sintorn; Morten Fjeld, Linköping University Electronic Press, 2014, p. 75-82Conference paper (Refereed)
    Abstract [en]

    A new mirror stereoscopic display for Direct Volume Rendering (DVR) is presented. The stereoscopic display system is composed of one monitor and one acrylic first surface mirror. The mirror reflects one image for one of the eyes. The geometrical transformations to compute correctly the stereo pair is presented and is the core of this paper. System considerations such as mirror placement and implications are also discussed.

    In contrast to other similar solutions, we do not use two monitors, but just one. Consequently one of the images needs to be skewed. Advantages of the system include absence of ghosting and of flickering.

    We also developed the rendering engine for DVR of volumetric datasets mostly for medical imaging visualization. The skewing process in this case is integrated into the ray casting of DVR. Using geometrical transformations, we can compute precisely the directions of the rays, producing accurate stereo pairs.

  • 247.
    Maria Marreiros, Filipe Miguel
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Smedby, Örjan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Single-Monitor-Mirror Stereoscopic Display2013In: Journal of Graphics Tools, ISSN 2165-347X, Vol. 17, no 3, p. 85-97Article in journal (Refereed)
    Abstract [en]

    A new single-monitor-mirror stereoscopic display is presented. The stereoscopic display system is composed of one monitor and one acrylic first-surface mirror. The mirror reflects one image for one of the eyes. The geometrical transformations to compute correctly the stereo pair are derived and presented. System considerations such as mirror placement and implications are also discussed.

    In contrast to other similar solutions that use fixed configurations, we try to optimize the display area by controlling the mirror placement. Consequently, one of the images needs to be skewed. Advantages of the system include absence of ghosting and flickering.

    We also developed the rendering engine for direct volume rendering (DVR) of volumetric datasets mostly for medical imaging visualization and using OpenGL for polygonal datasets and stereoscopic digital photography. The skewing process in this case is integrated into the ray-casting of DVR. Using geometrical transformations, we can compute precisely the directions of the rays, producing accurate stereo pairs. A similar operation is also performed using OpenGL.

  • 248.
    Maria Marreiros, Filipe Miguel
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Smedby, Örjan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Stereoscopic static depth perception of enclosed 3D objects2013In: SAP '13 Proceedings of the ACM Symposium on Applied Perception, New York, USA: Association for Computing Machinery (ACM), 2013, p. 15-22Conference paper (Refereed)
    Abstract [en]

    Depth perception of semi-transparent virtual objects and the visu-alization of their spatial layout are crucial in many applications, in particular medical applications. Depth cues for opaque objects have been extensively studied, but this is not the case for stereo-scopic semi-transparent objects, in particular in the case when one 3D object is enclosed within a larger exterior object.

    In this work we explored different stereoscopic rendering methodsto analyze their impact on depth perception accuracy of an enclosed3D object. Two experiments were performed: the first tested the hypotheses that depth perception is dependent on the color blending of objects (opacity - alpha) for each rendering method and that one of two rendering methods used is superior. The second experiment was performed to corroborate the results of the first experiment and to test an extra hypothesis: is depth perception improved if an auxiliary object that provides a relationship between the enclosed objectand the exterior is used?

    The first rendering method used is simple alpha blending with Blinn-Phong shading model, where a segmented brain (exterior object) and a synthetic tumor (enclosed object) were blended. The second rendering method also uses Blinn-Phong, but the shading was modified to preserve silhouettes and to provide an illustrative rendering. Comparing both rendering methods, the brighter regionsof the first rendering method will become more transparent in the second rendering method, thus preserving silhouette areas.

    The results show that depth perception accuracy of an enclosed object rendered with a stereoscopic system is dependent on opacity for some rendering methods (simple alpha blending), but this effect is less pronounced than the dependence on object position in relation to the exterior object. The illustrative rendering method is less dependent on opacity. The different rendering methods also perform slightly differently; an illustrative rendering method was superior and the use of an auxiliary object seems to facilitate depth perception.

  • 249.
    Markström, Johannes
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    3D Position Estimation of a Person of Interest in Multiple Video Sequences: People Detection2013Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In most cases today when a specific person's whereabouts is monitored through video surveillance it is done manually and his or her location when not seen is based on assumptions on how fast he or she can move. Since humans are good at recognizing people this can be done accurately, given good video data, but the time needed to go through all data is extensive and therefore expensive. Because of the rapid technical development computers are getting cheaper to use and therefore more interesting to use for tedious work.

    This thesis is a part of a larger project that aims to see to what extent it is possible to estimate a person of interest's time dependent 3D position, when seen in surveillance videos. The surveillance videos are recorded with non overlapping monocular cameras. Furthermore the project aims to see if the person of interest's movement, when position data is unavailable, could be predicted. The outcome of the project is a software capable of following a person of interest's movement with an error estimate visualized as an area indicating where the person of interest might be at a specific time.

    This thesis main focus is to implement and evaluate a people detector meant to be used in the project, reduce noise in position measurement, predict the position when the person of interest's location is unknown, and to evaluate the complete project.

    The project combines known methods in computer vision and signal processing and the outcome is a software that can be used on a normal PC running on a Windows operating system. The software implemented in the thesis use a Hough transform based people detector and a Kalman filter for one step ahead prediction. The detector is evaluated with known methods such as Miss-rate vs. False Positives per Window or Image (FPPW and FPPI respectively) and Recall vs. 1-Precision.

    The results indicate that it is possible to estimate a person of interest's 3D position with single monocular cameras. It is also possible to follow the movement, to some extent, were position data are unavailable. However the software needs more work in order to be robust enough to handle the diversity that may appear in different environments and to handle large scale sensor networks.

  • 250.
    Markus, Nenad
    et al.
    Faculty of Electrical Engineering and Computing, University of Zagreb.
    Gogic, Ivan
    Faculty of Electrical Engineering and Computing, University of Zagreb.
    Pandžic, Igor
    Faculty of Electrical Engineering and Computing, University of Zagreb.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Memory-efficient Global Refinement of Decision-Tree Ensembles and its Application to Face Alignment2018In: Proceedings of BMVC 2018 and Workshops, Newcastle upon Tyne, UK: The British Machine Vision Association and Society for Pattern Recognition , 2018, p. 1-11, article id 896Conference paper (Refereed)
    Abstract [en]

    Ren et al. [17] recently introduced a method for aggregating multiple decision trees into a strong predictor by interpreting a path taken by a sample down each tree as a binary vector and performing linear regression on top of these vectors stacked together. They provided experimental evidence that the method offers advantages over the usual approaches for combining decision trees (random forests and boosting). The method truly shines when the regression target is a large vector with correlated dimensions, such as a 2D face shape represented with the positions of several facial landmarks. However, we argue that their basic method is not applicable in many practical scenarios due to large memory requirements. This paper shows how this issue can be solved through the use of quantization and architectural changes of the predictor that maps decision tree-derived encodings to the desired output.

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