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Boosting Chamfer Matching by Learning Chamfer Distance Normalization

  • Tianyang Ma
  • Xingwei Yang
  • Longin Jan Latecki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6315)

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.

Keywords

Target Object Object Detection IEEE Conf Weak Learner Cluttered Background 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Tianyang Ma
    • 1
  • Xingwei Yang
    • 1
  • Longin Jan Latecki
    • 1
  1. 1.Dept. of Computer and Information SciencesTemple UnviersityPhiladelphia

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