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Proceedings Paper

Point pattern matching using modified ant colony optimization
Author(s): Yu Guo; Min Lu; Zhiguo Tan; Ge Ren
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Paper Abstract

This paper proposes an ant colony optimization (ACO) based approach for point pattern matching (PPM) under affine transformation. In the paper, the point sets matching problem is formulated as a mixed variable (binary and continuous) optimization problem. The ACO is used to search for the optimal transformation parameters. There are two contributions made in this paper. Firstly, we manage to modify the original ACO method by combining it with the leastsquares method. Thus, it can handle with the continuous spatial mapping parameters searching. Secondly, we introduce a threshold to correspondence finding, which rejects outliers and enhances veracity while using "Nearest Neighbors Search". Experiments demonstrate the validity and robustness of the algorithm.

Paper Details

Date Published: 30 October 2009
PDF: 7 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74960Y (30 October 2009); doi: 10.1117/12.833054
Show Author Affiliations
Yu Guo, National Univ. of Defense Technology (China)
Min Lu, National Univ. of Defense Technology (China)
Zhiguo Tan, National Univ. of Defense Technology (China)
Ge Ren, Key Lab. of Beam Control (China)

Published in SPIE Proceedings Vol. 7496:
MIPPR 2009: Pattern Recognition and Computer Vision
Mingyue Ding; Bir Bhanu; Friedrich M. Wahl; Jonathan Roberts, Editor(s)

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