Boosting chamfer matching by learning chamfer distance normalization

Authors:
Tianyang Ma;Xingwei Yang;Longin Jan Latecki
Affiliations:
Dept. of Computer and Information Sciences, Temple Unviersity, Philadelphia;Dept. of Computer and Information Sciences, Temple Unviersity, Philadelphia;Dept. of Computer and Information Sciences, Temple Unviersity, Philadelphia
Venue:
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Year:
2010

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Abstract

We propose a novel technique that significantly improves the performance of oriented chamfer matching on images with cluttered background. Different to other matching methods, which only measures how well a template fits to an edge map, we evaluate the score of the template in comparison to auxiliary contours, which we call normalizers. We utilize AdaBoost to learn a Normalized Oriented Chamfer Distance (NOCD). Our experimental results demonstrate that it boosts the detection rate of the oriented chamfer distance. The simplicity and ease of training of NOCD on a small number of training samples promise that it can replace chamfer distance and oriented chamfer distance in any template matching application.