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Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
National University Singapore.
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.ORCID iD: 0000-0002-9466-9826
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2010 (English)In: Proceedings of the 20th International Conferenceon Pattern Recognition, 2010, 302-306 p.Conference 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.

Place, publisher, year, edition, pages
2010. 302-306 p.
, International Conference on Pattern Recognition, ISSN 1051-4651
Keyword [en]
Torchlight, Pose estimation, Active illumination, Plane estimation, Ellipses
National Category
Control Engineering
URN: urn:nbn:se:liu:diva-60597DOI: 10.1109/ICPR.2010.83ISBN: 978-1-4244-7542-1ISBN: 978-0-7695-4109-9OAI: diva2:358053
20th International Conference on Pattern Recognition, Istanbul, Turkey, 23-26 August, 2010
Swedish Foundation for Strategic Research
Available from: 2010-10-20 Created: 2010-10-20 Last updated: 2015-09-22Bibliographically approved
In thesis
1. Shape Based Recognition – Cognitive Vision Systems in Traffic Safety Applications
Open this publication in new window or tab >>Shape Based Recognition – Cognitive Vision Systems in Traffic Safety Applications
2011 (English)Doctoral 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.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. 49 p.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1395
National Category
Computer Vision and Robotics (Autonomous Systems)
urn:nbn:se:liu:diva-71664 (URN)978-91-7393-074-1 (ISBN)
Public defence
2011-11-25, Vallfarten, hus Vallfarten, Campus Valla, Linköpings universitet, Linköping, 09:15 (English)
Available from: 2011-10-28 Created: 2011-10-28 Last updated: 2011-10-31Bibliographically approved

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