Point Pattern Matching

  • A. Ardeshir Goshtasby
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)


Various point pattern matching algorithms are reviewed and compared. Among the matching algorithms discussed are random sample and consensus (RANSAC), graph-based, feature-based, clustering-based, invariance-based, axis of minimum inertia-based, relaxation-based, and spectral graph theory-based algorithms. To speed up the matching process, the coarse-to-fine search strategy is also discussed and its use in matching of point patterns with nonlinear geometric differences is demonstrated. Also included in this chapter are detailed matching algorithms and methods to determine their performances.


Projective Transformation Point Pair Interior Edge Voronoi Region Proximity Matrix 
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 London Limited 2012

Authors and Affiliations

  1. 1.Dept. Computer Science and Engineering, 303 Russ Engineering CenterWright State UniversityDaytonUSA

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