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Correlating Fourier descriptors of local patches for road sign recognition
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.ORCID iD: 0000-0002-6096-3648
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-5698-5983
2011 (English)In: IET Computer Vision, ISSN 1751-9632, E-ISSN 1751-9640, Vol. 5, no 4, 244-254 p.Article 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. Vol. 5, no 4, 244-254 p.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-65621DOI: 10.1049/iet-cvi.2010.0040ISI: 000291385900007OAI: oai:DiVA.org:liu-65621DiVA: diva2:397235
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
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.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1395
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
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)
Opponent
Supervisors
Available from: 2011-10-28 Created: 2011-10-28 Last updated: 2016-05-04Bibliographically approved

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Larsson, FredrikFelsberg, MichaelForssen, Per-Erik

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