liu.seSearch for publications in DiVA
Change search
Link to record
Permanent link

Direct link
BETA
Alternative names
Publications (10 of 171) Show all publications
Holmquist, K., Senel, O. & Felsberg, M. (2018). Computing a Collision-Free Path using the monogenic scale space. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): . Paper presented at IROS 2018, Madrid, Spain, October 1-5, 2018 (pp. 8097-8102). IEEE
Open this publication in new window or tab >>Computing a Collision-Free Path using the monogenic scale space
2018 (English)In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2018, p. 8097-8102Conference paper, Published paper (Refereed)
Abstract [en]

Mobile robots have been used for various purposes with different functionalities which require them to freely move in environments containing both static and dynamic obstacles to accomplish given tasks. One of the most relevant capabilities in terms of navigating a mobile robot in such an environment is to find a safe path to a goal position. This paper shows that there exists an accurate solution to the Laplace equation which allows finding a collision-free path and that it can be efficiently calculated for a rectangular bounded domain such as a map which is represented as an image. This is accomplished by the use of the monogenic scale space resulting in a vector field which describes the attracting and repelling forces from the obstacles and the goal. The method is shown to work in reasonably convex domains and by the use of tessellation of the environment map for non-convex environments.

Place, publisher, year, edition, pages
IEEE, 2018
Series
International Conference on Intelligent Robots and Systems (IROS), ISSN 2153-0858
National Category
Computer Vision and Robotics (Autonomous Systems) Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-152713 (URN)10.1109/IROS.2018.8593583 (DOI)978-1-5386-8094-0 (ISBN)978-1-5386-8095-7 (ISBN)978-1-5386-8093-3 (ISBN)
Conference
IROS 2018, Madrid, Spain, October 1-5, 2018
Note

Funding agencies:This work was founded by the European Union's Horizon 2020 Programme under grant agreement 644839 (CEN-TAURO).

Available from: 2018-11-16 Created: 2018-11-16 Last updated: 2019-03-20
Danelljan, M., Bhat, G., Gladh, S., Khan, F. S. & Felsberg, M. (2018). Deep motion and appearance cues for visual tracking. Pattern Recognition Letters
Open this publication in new window or tab >>Deep motion and appearance cues for visual tracking
Show others...
2018 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344Article in journal (Refereed) Published
Abstract [en]

Generic visual tracking is a challenging computer vision problem, with numerous applications. Most existing approaches rely on appearance information by employing either hand-crafted features or deep RGB features extracted from convolutional neural networks. Despite their success, these approaches struggle in case of ambiguous appearance information, leading to tracking failure. In such cases, we argue that motion cue provides discriminative and complementary information that can improve tracking performance. Contrary to visual tracking, deep motion features have been successfully applied for action recognition and video classification tasks. Typically, the motion features are learned by training a CNN on optical flow images extracted from large amounts of labeled videos. In this paper, we investigate the impact of deep motion features in a tracking-by-detection framework. We also evaluate the fusion of hand-crafted, deep RGB, and deep motion features and show that they contain complementary information. To the best of our knowledge, we are the first to propose fusing appearance information with deep motion features for visual tracking. Comprehensive experiments clearly demonstrate that our fusion approach with deep motion features outperforms standard methods relying on appearance information alone.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Visual tracking, Deep learning, Optical flow, Discriminative correlation filters
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:liu:diva-148015 (URN)10.1016/j.patrec.2018.03.009 (DOI)2-s2.0-85044328745 (Scopus ID)
Available from: 2018-05-24 Created: 2018-05-24 Last updated: 2018-05-31Bibliographically approved
Järemo Lawin, F., Danelljan, M., Khan, F. S., Forssén, P.-E. & Felsberg, M. (2018). Density Adaptive Point Set Registration. In: : . Paper presented at The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, United States, 18-22 June, 2018.
Open this publication in new window or tab >>Density Adaptive Point Set Registration
Show others...
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Probabilistic methods for point set registration have demonstrated competitive results in recent years. These techniques estimate a probability distribution model of the point clouds. While such a representation has shown promise, it is highly sensitive to variations in the density of 3D points. This fundamental problem is primarily caused by changes in the sensor location across point sets.    We revisit the foundations of the probabilistic registration paradigm. Contrary to previous works, we model the underlying structure of the scene as a latent probability distribution, and thereby induce invariance to point set density changes. Both the probabilistic model of the scene and the registration parameters are inferred by minimizing the Kullback-Leibler divergence in an Expectation Maximization based framework. Our density-adaptive registration successfully handles severe density variations commonly encountered in terrestrial Lidar applications. We perform extensive experiments on several challenging real-world Lidar datasets. The results demonstrate that our approach outperforms state-of-the-art probabilistic methods for multi-view registration, without the need of re-sampling.

National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-149774 (URN)
Conference
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, United States, 18-22 June, 2018
Available from: 2018-07-18 Created: 2018-07-18 Last updated: 2018-10-10Bibliographically approved
Berg, A., Ahlberg, J. & Felsberg, M. (2018). Generating Visible Spectrum Images from Thermal Infrared. In: : . Paper presented at The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (pp. 1143-1152). Salt Lake City, USA
Open this publication in new window or tab >>Generating Visible Spectrum Images from Thermal Infrared
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Transformation of thermal infrared (TIR) images into visual, i.e. perceptually realistic color (RGB) images, is a challenging problem. TIR cameras have the ability to see in scenarios where vision is severely impaired, for example in total darkness or fog, and they are commonly used, e.g., for surveillance and automotive applications. However, interpretation of TIR images is difficult, especially for untrained operators. Enhancing the TIR image display by transforming it into a plausible, visual, perceptually realistic RGB image presumably facilitates interpretation. Existing grayscale to RGB, so called, colorization methods cannot be applied to TIR images directly since those methods only estimate the chrominance and not the luminance. In the absence of conventional colorization methods, we propose two fully automatic TIR to visual color image transformation methods, a two-step and an integrated approach, based on Convolutional Neural Networks. The methods require neither pre- nor postprocessing, do not require any user input, and are robust to image pair misalignments. We show that the methods do indeed produce perceptually realistic results on publicly available data, which is assessed both qualitatively and quantitatively.

Place, publisher, year, edition, pages
Salt Lake City, USA: , 2018
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-149429 (URN)
Conference
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops
Funder
Swedish Research Council, 2013-5703Swedish Research Council, 2014-6227
Available from: 2018-06-29 Created: 2018-06-29 Last updated: 2018-08-24
Grelsson, B. & Felsberg, M. (2018). Improved Learning in Convolutional Neural Networks with Shifted Exponential Linear Units (ShELUs). In: 2018 24th International Conference on Pattern Recognition (ICPR): . Paper presented at 24th International Conference on Pattern Recognition, ICPR 2018, Beijing, China, 20-24 Aug. 2018 (pp. 517-522). IEEE
Open this publication in new window or tab >>Improved Learning in Convolutional Neural Networks with Shifted Exponential Linear Units (ShELUs)
2018 (English)In: 2018 24th International Conference on Pattern Recognition (ICPR), IEEE, 2018, p. 517-522Conference paper, Published paper (Refereed)
Abstract [en]

The Exponential Linear Unit (ELU) has been proven to speed up learning and improve the classification performance over activation functions such as ReLU and Leaky ReLU for convolutional neural networks. The reasons behind the improved behavior are that ELU reduces the bias shift, it saturates for large negative inputs and it is continuously differentiable. However, it remains open whether ELU has the optimal shape and we address the quest for a superior activation function.We use a new formulation to tune a piecewise linear activation function during training, to investigate the above question, and learn the shape of the locally optimal activation function. With this tuned activation function, the classification performance is improved and the resulting, learned activation function shows to be ELU-shaped irrespective if it is initialized as a RELU, LReLU or ELU. Interestingly, the learned activation function does not exactly pass through the origin indicating that a shifted ELU-shaped activation function is preferable. This observation leads us to introduce the Shifted Exponential Linear Unit (ShELU) as a new activation function.Experiments on Cifar-100 show that the classification performance is further improved when using the ShELU activation function in comparison with ELU. The improvement is achieved when learning an individual bias shift for each neuron.

Place, publisher, year, edition, pages
IEEE, 2018
Series
International Conference on Pattern Recognition
Keywords
CNN, activation function
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-151606 (URN)10.1109/ICPR.2018.8545104 (DOI)000455146800087 ()978-1-5386-3787-6 (ISBN)
Conference
24th International Conference on Pattern Recognition, ICPR 2018, Beijing, China, 20-24 Aug. 2018
Funder
Wallenberg Foundations
Note

Funding agencies:  Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation; Swedish Research Council [2014-6227]

Available from: 2018-09-27 Created: 2018-09-27 Last updated: 2019-04-12
Felsberg, M. (2018). Probabilistic and biologically inspired feature representations. San Rafael: Morgan & Claypool Publishers
Open this publication in new window or tab >>Probabilistic and biologically inspired feature representations
2018 (English)Book (Refereed)
Abstract [en]

Under the title "Probabilistic and Biologically Inspired Feature Representations," this text collects a substantial amount of work on the topic of channel representations. Channel representations are a biologically motivated, wavelet-like approach to visual feature descriptors: they are local and compact, they form a computational framework, and the represented information can be reconstructed. The first property is shared with many histogram- and signature-based descriptors, the latter property with the related concept of population codes. In their unique combination of properties, channel representations become a visual Swiss army knife—they can be used for image enhancement, visual object tracking, as 2D and 3D descriptors, and for pose estimation. In the chapters of this text, the framework of channel representations will be introduced and its attributes will be elaborated, as well as further insight into its probabilistic modeling and algorithmic implementation will be given. Channel representations are a useful toolbox to represent visual information for machine learning, as they establish a generic way to compute popular descriptors such as HOG, SIFT, and SHOT. Even in an age of deep learning, they provide a good compromise between hand-designed descriptors and a-priori structureless feature spaces as seen in the layers of deep networks.

Place, publisher, year, edition, pages
San Rafael: Morgan & Claypool Publishers, 2018. p. 103
Series
Synthesis Lectures on Computer Vision, ISSN 2153-1056, E-ISSN 2153-1064 ; 8(2)
Keywords
Computer vision, Pattern recognition systems, Bildbehandling
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-148136 (URN)10.2200/S00851ED1V01Y201804COV016 (DOI)9781681730233 (ISBN)9781681733661 (ISBN)9781681730240 (ISBN)
Projects
EMC2, WASP, ELLIIT, CENTAURO, SymbiCloud, CYCLA
Available from: 2018-05-31 Created: 2018-05-31 Last updated: 2018-11-07Bibliographically approved
Eldesokey, A., Felsberg, M. & Khan, F. S. (2018). Propagating Confidences through CNNs for Sparse Data Regression. In: : . Paper presented at The 29th British Machine Vision Conference (BMVC), Northumbria University, Newcastle upon Tyne, England, UK, 3-6 September, 2018.
Open this publication in new window or tab >>Propagating Confidences through CNNs for Sparse Data Regression
2018 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

In most computer vision applications, convolutional neural networks (CNNs) operate on dense image data generated by ordinary cameras. Designing CNNs for sparse and irregularly spaced input data is still an open problem with numerous applications in autonomous driving, robotics, and surveillance. To tackle this challenging problem, we introduce an algebraically-constrained convolution layer for CNNs with sparse input and demonstrate its capabilities for the scene depth completion task. We propose novel strategies for determining the confidence from the convolution operation and propagating it to consecutive layers. Furthermore, we propose an objective function that simultaneously minimizes the data error while maximizing the output confidence. Comprehensive experiments are performed on the KITTI depth benchmark and the results clearly demonstrate that the proposed approach achieves superior performance while requiring three times fewer parameters than the state-of-the-art methods. Moreover, our approach produces a continuous pixel-wise confidence map enabling information fusion, state inference, and decision support.

National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-149648 (URN)
Conference
The 29th British Machine Vision Conference (BMVC), Northumbria University, Newcastle upon Tyne, England, UK, 3-6 September, 2018
Available from: 2018-07-13 Created: 2018-07-13 Last updated: 2018-10-09Bibliographically approved
Felsberg, M., Heyden, A. & Krüger, N. (Eds.). (2017). Computer Analysis of Images and Patterns: 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part I. Paper presented at 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part I. Springer
Open this publication in new window or tab >>Computer Analysis of Images and Patterns: 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part I
2017 (English)Conference proceedings (editor) (Refereed)
Abstract [en]

The two volume set LNCS 10424 and 10425 constitutes the refereed proceedings of the 17th International Conference on Computer Analysis of Images and Patterns, CAIP 2017, held in Ystad, Sweden, in August 2017.

The 72 papers presented were carefully reviewed and selected from 144 submissions The papers are organized in the following topical sections: Vision for Robotics; Motion and Tracking; Segmentation; Image/Video Indexing and Retrieval; Shape Representation and Analysis; Biomedical Image Analysis; Biometrics; Machine Learning; Image Restoration; and Poster Sessions.

Place, publisher, year, edition, pages
Springer, 2017. p. 413
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10424
Keywords
Computer vision, Image segmentation, Object recognition, Image reconstruction, Biomedical image and pattern analysis, Brain-inspired methods, Face and gestures, Feature extraction and key-point detection, Graph-based methods, High-dimensional topology methods, Human pose estimation, Image/video indexing and retrieval, Image restoration, Image processing, Machine learning for image and pattern analysis, Mobile multimedia, Motion and tracking, Shape representation and analysis, Vision for robotics
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Sciences
Identifiers
urn:nbn:se:liu:diva-145365 (URN)10.1007/978-3-319-64689-3 (DOI)9783319646886 (ISBN)9783319646893 (ISBN)
Conference
17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part I
Available from: 2018-02-26 Created: 2018-02-26 Last updated: 2018-02-26Bibliographically approved
Felsberg, M., Heyden, A. & Krüger, N. (Eds.). (2017). Computer Analysis of Images and Patterns: 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part II. Paper presented at 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part II. Springer
Open this publication in new window or tab >>Computer Analysis of Images and Patterns: 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part II
2017 (English)Conference proceedings (editor) (Refereed)
Abstract [en]

The two volume set LNCS 10424 and 10425 constitutes the refereed proceedings of the 17th International Conference on Computer Analysis of Images and Patterns, CAIP 2017, held in Ystad, Sweden, in August 2017.  The 72 papers presented were carefully reviewed and selected from 144 submissions The papers are organized in the following topical sections: Vision for Robotics; Motion and Tracking; Segmentation; Image/Video Indexing and Retrieval; Shape Representation and Analysis; Biomedical Image Analysis; Biometrics; Machine Learning; Image Restoration; and Poster Sessions.

Place, publisher, year, edition, pages
Springer, 2017. p. 487
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10425
Keywords
Computer vision, Image segmentation, Object recognition, Image reconstruction, Biomedical image and pattern analysis, Brain-inspired methods, Face and gestures, Feature extraction and key-point detection, Graph-based methods, High-dimensional topology methods, Human pose estimation, Image/video indexing and retrieval, Image restoration, Image processing, Machine learning for image and pattern analysis, Mobile multimedia, Motion and tracking, Shape representation and analysis, Vision for robotics
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Engineering
Identifiers
urn:nbn:se:liu:diva-145370 (URN)10.1007/978-3-319-64698-5 (DOI)9783319646978 (ISBN)9783319646985 (ISBN)
Conference
17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part II
Available from: 2018-02-26 Created: 2018-02-26 Last updated: 2018-02-26Bibliographically approved
Johnander, J., Danelljan, M., Khan, F. S. & Felsberg, M. (2017). DCCO: Towards Deformable Continuous Convolution Operators for Visual Tracking. In: Michael Felsberg, Anders Heyden and Norbert Krüger (Ed.), Computer Analysis of Images and Patterns: 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part I. Paper presented at 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part I (pp. 55-67). Springer, 10424
Open this publication in new window or tab >>DCCO: Towards Deformable Continuous Convolution Operators for Visual Tracking
2017 (English)In: Computer Analysis of Images and Patterns: 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part I / [ed] Michael Felsberg, Anders Heyden and Norbert Krüger, Springer, 2017, Vol. 10424, p. 55-67Conference paper, Published paper (Refereed)
Abstract [en]

Discriminative Correlation Filter (DCF) based methods have shown competitive performance on tracking benchmarks in recent years. Generally, DCF based trackers learn a rigid appearance model of the target. However, this reliance on a single rigid appearance model is insufficient in situations where the target undergoes non-rigid transformations. In this paper, we propose a unified formulation for learning a deformable convolution filter. In our framework, the deformable filter is represented as a linear combination of sub-filters. Both the sub-filter coefficients and their relative locations are inferred jointly in our formulation. Experiments are performed on three challenging tracking benchmarks: OTB-2015, TempleColor and VOT2016. Our approach improves the baseline method, leading to performance comparable to state-of-the-art.

Place, publisher, year, edition, pages
Springer, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10424
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Engineering
Identifiers
urn:nbn:se:liu:diva-145373 (URN)10.1007/978-3-319-64689-3_5 (DOI)000432085900005 ()9783319646886 (ISBN)9783319646893 (ISBN)
Conference
17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part I
Note

Funding agencies: SSF (SymbiCloud); VR (EMC2) [2016-05543]; SNIC; WASP; Nvidia

Available from: 2018-02-26 Created: 2018-02-26 Last updated: 2018-10-16Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-6096-3648

Search in DiVA

Show all publications