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  • 1.
    Danelljan, Martin
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Robinson, Andreas
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Khan, Fahad Shahbaz
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking2016Ingår i: Computer Vision – ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part V / [ed] Bastian Leibe, Jiri Matas, Nicu Sebe and Max Welling, Cham: Springer, 2016, s. 472-488Konferensbidrag (Refereegranskat)
    Abstract [en]

    Discriminative Correlation Filters (DCF) have demonstrated excellent performance for visual object tracking. The key to their success is the ability to efficiently exploit available negative data by including all shifted versions of a training sample. However, the underlying DCF formulation is restricted to single-resolution feature maps, significantly limiting its potential. In this paper, we go beyond the conventional DCF framework and introduce a novel formulation for training continuous convolution filters. We employ an implicit interpolation model to pose the learning problem in the continuous spatial domain. Our proposed formulation enables efficient integration of multi-resolution deep feature maps, leading to superior results on three object tracking benchmarks: OTB-2015 (+5.1% in mean OP), Temple-Color (+4.6% in mean OP), and VOT2015 (20% relative reduction in failure rate). Additionally, our approach is capable of sub-pixel localization, crucial for the task of accurate feature point tracking. We also demonstrate the effectiveness of our learning formulation in extensive feature point tracking experiments.

  • 2.
    Grelsson, Bertil
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Robinson, Andreas
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Khan, Fahad Shahbaz
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    HorizonNet for visual terrain navigation2018Ingår i: Proceedings of 2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS), Institute of Electrical and Electronics Engineers (IEEE), 2018, s. 149-155Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper investigates the problem of position estimation of unmanned surface vessels (USVs) operating in coastal areas or in the archipelago. We propose a position estimation method where the horizon line is extracted in a 360 degree panoramic image around the USV. We design a CNN architecture to determine an approximate horizon line in the image and implicitly determine the camera orientation (the pitch and roll angles). The panoramic image is warped to compensate for the camera orientation and to generate an image from an approximately level camera. A second CNN architecture is designed to extract the pixelwise horizon line in the warped image. The extracted horizon line is correlated with digital elevation model (DEM) data in the Fourier domain using a MOSSE correlation filter. Finally, we determine the location of the maximum correlation score over the search area to estimate the position of the USV. Comprehensive experiments are performed in a field trial in the archipelago. Our approach provides promising results by achieving position estimates with GPS-level accuracy.

  • 3.
    Hedborg, Johan
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Robinson, Andreas
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Robust Three-View Triangulation Done Fast2014Ingår i: Proceedings: 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014, IEEE , 2014, s. 152-157Konferensbidrag (Refereegranskat)
    Abstract [en]

    Estimating the position of a 3-dimensional world point given its 2-dimensional projections in a set of images is a key component in numerous computer vision systems. There are several methods dealing with this problem, ranging from sub-optimal, linear least square triangulation in two views, to finding the world point that minimized the L2-reprojection error in three views. This leads to the statistically optimal estimate under the assumption of Gaussian noise. In this paper we present a solution to the optimal triangulation in three views. The standard approach for solving the three-view triangulation problem is to find a closed-form solution. In contrast to this, we propose a new method based on an iterative scheme. The method is rigorously tested on both synthetic and real image data with corresponding ground truth, on a midrange desktop PC and a Raspberry Pi, a low-end mobile platform. We are able to improve the precision achieved by the closed-form solvers and reach a speed-up of two orders of magnitude compared to the current state-of-the-art solver. In numbers, this amounts to around 300K triangulations per second on the PC and 30K triangulations per second on Raspberry Pi.

  • 4.
    Kristan, Matej
    et al.
    University of Ljubljana, Slovenia.
    Leonardis, Ales
    University of Birmingham, England.
    Matas, Jiri
    Czech Technical University, Czech Republic.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Pflugfelder, Roman
    Austrian Institute Technology, Austria.
    Cehovin, Luka
    University of Ljubljana, Slovenia.
    Vojir, Tomas
    Czech Technical University, Czech Republic.
    Häger, Gustav
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    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
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    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
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    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
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    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 Results2016Ingår i: COMPUTER VISION - ECCV 2016 WORKSHOPS, PT II, SPRINGER INT PUBLISHING AG , 2016, Vol. 9914, s. 777-823Konferensbidrag (Refereegranskat)
    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.

  • 5.
    Robinson, Andreas
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Persson, Mikael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Robust Accurate Extrinsic Calibration of Static Non-overlapping Cameras2017Ingår i: Computer Analysis of Images and Patterns: 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part II / [ed] Michael Felsberg, Anders Heyden and Norbert Krüger, Springer, 2017, Vol. 10425, s. 342-353Konferensbidrag (Refereegranskat)
    Abstract [en]

    An increasing number of robots and autonomous vehicles are equipped with multiple cameras to achieve surround-view sensing. The estimation of their relative poses, also known as extrinsic parameter calibration, is a challenging problem, particularly in the non-overlapping case. We present a simple and novel extrinsic calibration method based on standard components that performs favorably to existing approaches. We further propose a framework for predicting the performance of different calibration configurations and intuitive error metrics. This makes selecting a good camera configuration straightforward. We evaluate on rendered synthetic images and show good results as measured by angular and absolute pose differences, as well as the reprojection error distributions.

  • 6.
    Öfjäll, Kristoffer
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Robinson, Andreas
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Visual Autonomous Road Following by Symbiotic Online Learning2016Ingår i: Intelligent Vehicles Symposium (IV), 2016 IEEE, 2016, s. 136-143Konferensbidrag (Refereegranskat)
    Abstract [en]

    Recent years have shown great progress in driving assistance systems, approaching autonomous driving step by step. Many approaches rely on lane markers however, which limits the system to larger paved roads and poses problems during winter. In this work we explore an alternative approach to visual road following based on online learning. The system learns the current visual appearance of the road while the vehicle is operated by a human. When driving onto a new type of road, the human driver will drive for a minute while the system learns. After training, the human driver can let go of the controls. The present work proposes a novel approach to online perception-action learning for the specific problem of road following, which makes interchangeably use of supervised learning (by demonstration), instantaneous reinforcement learning, and unsupervised learning (self-reinforcement learning). The proposed method, symbiotic online learning of associations and regression (SOLAR), extends previous work on qHebb-learning in three ways: priors are introduced to enforce mode selection and to drive learning towards particular goals, the qHebb-learning methods is complemented with a reinforcement variant, and a self-assessment method based on predictive coding is proposed. The SOLAR algorithm is compared to qHebb-learning and deep learning for the task of road following, implemented on a model RC-car. The system demonstrates an ability to learn to follow paved and gravel roads outdoors. Further, the system is evaluated in a controlled indoor environment which provides quantifiable results. The experiments show that the SOLAR algorithm results in autonomous capabilities that go beyond those of existing methods with respect to speed, accuracy, and functionality. 

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