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Proceedings Paper

Fast-match on particle swarm optimization with variant system mechanism
Author(s): Yuehuang Wang; Xin Fang; Jie Chen
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Paper Abstract

Fast-Match is a fast and effective algorithm for approximate template matching under 2D affine transformations, which can match the target with maximum similarity without knowing the target gesture. It depends on the minimum Sum-of-Absolute-Differences (SAD) error to obtain the best affine transformation. The algorithm is widely used in the field of matching images because of its fastness and robustness. In this paper, our approach is to search an approximate affine transformation over Particle Swarm Optimization (PSO) algorithm. We treat each potential transformation as a particle that possesses memory function. Each particle is given a random speed and flows throughout the 2D affine transformation space. To accelerate the algorithm and improve the abilities of seeking the global excellent result, we have introduced the variant system mechanism on this basis. The benefit is that we can avoid matching with huge amount of potential transformations and falling into local optimal condition, so that we can use a few transformations to approximate the optimal solution. The experimental results prove that our method has a faster speed and a higher accuracy performance with smaller affine transformation space.

Paper Details

Date Published: 8 March 2018
PDF: 7 pages
Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106090W (8 March 2018); doi: 10.1117/12.2284986
Show Author Affiliations
Yuehuang Wang, Huazhong Univ. of Science and Technology (China)
National Key Lab. of Science and Technology on Multi-spectral Information Processing (China)
Xin Fang, Huazhong Univ. of Science and Technology (China)
Jie Chen, China Aerospace Science and Industry Corp. (China)


Published in SPIE Proceedings Vol. 10609:
MIPPR 2017: Pattern Recognition and Computer Vision
Zhiguo Cao; Yuehuang Wang; Chao Cai, Editor(s)

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