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Proceedings Paper

Multi-threshold image segmentation with improved quantum-inspired genetic algorithm
Author(s): Xiaowei Fu; Mingyue Ding; Chengping Zhou; Yangguang Sun
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Paper Abstract

In this paper, a method of multi-threshold image segmentation was proposed using the principle of maximum entropy and an improved quantum-inspired genetic algorithm (IQGA). With the increase number of multi-threshold, it is unrealistic to compute the entropy of all possible combinations and find the maximum entropy in all the multi-threshold combinations for images segmentation. Quantum-inspired genetic algorithm (QGA) has a better characteristic of population diversity, rapid convergence and global search capability than that of the conventional genetic algorithm (CGA). However, the solutions of QGAs may diverge or have a premature convergence to a local optimum due to the selection of the rotation angle in searching the maximum value of a function. Therefore, IQGA is put forward which joins the optimal selection and catastrophe operations, and defines an adaptive rotation angle of quantum gate during quantum chromosomes update procedure. Experimental results demonstrated that the proposed method has a good performance.

Paper Details

Date Published: 30 October 2009
PDF: 7 pages
Proc. SPIE 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis, 749518 (30 October 2009); doi: 10.1117/12.839978
Show Author Affiliations
Xiaowei Fu, Huazhong Univ. of Science and Technology (China)
Wuhan Univ. of Science and Technology (China)
Mingyue Ding, Huazhong Univ. of Science and Technology (China)
Chengping Zhou, Huazhong Univ. of Science and Technology (China)
Yangguang Sun, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 7495:
MIPPR 2009: Automatic Target Recognition and Image Analysis
Tianxu Zhang; Bruce Hirsch; Zhiguo Cao; Hanqing Lu, Editor(s)

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