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Regular polygon detection
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
2005 (English)In: Proceedings - 10th IEEE International Conference on Computer Vision, IEEE Computer Society, 2005, 778-785 p.Conference paper (Refereed)
Abstract [en]

This paper describes a new robust regular polygon detector. The regular polygon transform is posed as a mixture of regular polygons in a five dimensional space. Given the edge structure of an image, we derive the a posteriori probability for a mixture of regular polygons, and thus the probability density function for the appearance of a mixture of regular polygons. Likely regular polygons can be isolated quickly by discretising and collapsing the search space into three dimensions. The remaining dimensions may be efficiently recovered subsequently using maximum likelihood at the locations of the most likely polygons in the subspace. This leads to an efficient algorithm. Also the a posteriori formulation facilitates inclusion of additional a priori information leading to real-time application to road sign detection. The use of gradient information also reduces noise compared to existing approaches such as the generalised Hough transform. Results are presented for images with noise to show stability. The detector is also applied to two separate applications: real-time road sign detection for on-line driver assistance; and feature detection, recovering stable features in rectilinear environments.

Place, publisher, year, edition, pages
IEEE Computer Society, 2005. 778-785 p.
, IEEE International Conference on Computer Vision. Proceedings, ISSN 1550-5499
Keyword [en]
Edge structure, Five dimensional space, Polygons, Robust regular polygon, Acoustic noise, Algorithms, Hough transforms, Information analysis, Stability, Three dimensional, Probability density function
National Category
Computer and Information Science
URN: urn:nbn:se:kth:diva-156568DOI: 10.1109/ICCV.2005.207ISI: 000233155100101ScopusID: 2-s2.0-33745950114ISBN: 0-7695-2334-XISBN: 978-076952334-7OAI: diva2:767297
Proceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005, 17 October 2005 through 20 October 2005, Beijing, China

QC 20141201

Available from: 2014-12-01 Created: 2014-12-01 Last updated: 2014-12-01Bibliographically approved

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Loy, Gareth
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