Please use this identifier to cite or link to this item: https://doi.org/10.1109/CVPR.2012.6248080
Title: Contour-based recognition
Authors: Xu, Y.
Quan, Y.
Zhang, Z.
Ji, H. 
Fermuller, C.
Nishigaki, M.
Dementhon, D.
Issue Date: 2012
Citation: Xu, Y.,Quan, Y.,Zhang, Z.,Ji, H.,Fermuller, C.,Nishigaki, M.,Dementhon, D. (2012). Contour-based recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition : 3402-3409. ScholarBank@NUS Repository. https://doi.org/10.1109/CVPR.2012.6248080
Abstract: Contour is an important cue for object recognition. In this paper, built upon the concept of torque in image space, we propose a new contour-related feature to detect and describe local contour information in images. There are two components for our proposed feature: One is a contour patch detector for detecting image patches with interesting information of object contour, which we call the Maximal/Minimal Torque Patch (MTP) detector. The other is a contour patch descriptor for characterizing a contour patch by sampling the torque values, which we call the Multi-scale Torque (MST) descriptor. Experiments for object recognition on the Caltech-101 dataset showed that the proposed contour feature outperforms other contour-related features and is on a par with many other types of features. When combing our descriptor with the complementary SIFT descriptor, impressive recognition results are observed. © 2012 IEEE.
Source Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
URI: http://scholarbank.nus.edu.sg/handle/10635/104549
ISBN: 9781467312264
ISSN: 10636919
DOI: 10.1109/CVPR.2012.6248080
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