Genetic algorithm for affine point pattern matching

被引：70

作者：

Zhang, LH ^{[1
]}

Xu, WL ^{[1
]}

Chang, C ^{[1
]}

机构：

[1] Tsing Hua Univ, Dept Automat, Beijing 100084, Peoples R China

来源：

PATTERN RECOGNITION LETTERS | 2003年 / 24卷 / 1-3期

关键词：

point pattern matching; affine transformation; genetic algorithm; partial Hausdorff distance; feature ellipses;

D O I：

10.1016/S0167-8655(02)00160-5

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (C) 2002 Elsevier Science B.V. All rights reserved.

引用

页码：9 / 19

页数：11

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