Genetic algorithm for affine point pattern matching

Authors:
Lihua Zhang;Wenli Xu;Cheng Chang
Affiliations:
Department of Automation, Tsinghua University, Beijing 100084, PR China;Department of Automation, Tsinghua University, Beijing 100084, PR China;Department of Automation, Tsinghua University, Beijing 100084, PR China
Venue:
Pattern Recognition Letters
Year:
2003

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Abstract

Point pattern matching (PPM) is an important topic in the fields of computer vision and pattern recognition. According to if there exists a one to one mapping between the two point sets to be matched, PPM can be divided into the case of complete matching and the case of incomplete matching. According to if utilizing information other than 2-D image coordinates, PPM can be divided into labelled point-matching case and unlabelled point-matching case. Using partial Hausdorff distance, this paper presents a genetic algorithm (GA) based method to solve the incomplete unlabelled matching problem under general affine transformation. Since it successfully reduces the solution space of GA by constructing 'feature ellipses' of point sets, the method can achieve high computing efficiency and good matching results. Theoretical analysis and simulation results show that the new algorithm is very effective.