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|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|
|Appears in Collections:||Staff Publications|
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