Kernel Spectral Correspondence Matching Using Label Consistency Constraints

  • Hongfang Wang
  • Edwin R. Hancock
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)

Abstract

This paper investigates a kernel spectral approach to the problem of point pattern matching. Our first contribution is to show how kernel principal components analysis can be effectively used for solving the point correspondence matching problem when the point-sets are subject to structural errors, i.e. they are of different size. Our second contribution is to show how label consistency constraints can be incorporated into the construction of the Gram matrices for solving the articulated point pattern matching problem. We compare our algorithm with earlier point matching approaches and provide experiments on both synthetic data and real world data.

Keywords

Feature Point Label Probability Kernel Principal Component Analysis Proximity Matrix Rigid Component 
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 2005

Authors and Affiliations

  • Hongfang Wang
    • 1
  • Edwin R. Hancock
    • 1
  1. 1.Dept. of Computer ScienceUniversity of YorkHeslington, YorkUK

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