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Proceedings Paper

Effective and fully automatic image segmentation using quantum entropy and pulse-coupled neural networks
Author(s): Songlin Du; Yaping Yan; Yide Ma
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Paper Abstract

A novel image segmentation algorithm which uses quantum entropy and pulse-coupled neural networks (PCNN) is proposed in this paper. Optimal iteration of the PCNN is one of the key factors affecting segmentation accuracy. We borrow quantum entropy from quantum information to act as a criterion in determining optimal iteration of the PCNN. Optimal iteration is captured while total quantum entropy of the segments reaches a maximum. Moreover, compared with other PCNN-employed algorithms, the proposed algorithm works without any manual intervention, because all parameters of the PCNN are set automatically. Experimental results prove that the proposed method can achieve much lower probabilities of error segmentation than other PCNN-based image segmentation algorithms, and this suggests that higher image segmentation quality is achieved by the proposed method.

Paper Details

Date Published: 4 March 2015
PDF: 8 pages
Proc. SPIE 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 944315 (4 March 2015); doi: 10.1117/12.2179096
Show Author Affiliations
Songlin Du, Lanzhou Univ. (China)
Yaping Yan, Lanzhou Univ. (China)
Yide Ma, Lanzhou Univ. (China)


Published in SPIE Proceedings Vol. 9443:
Sixth International Conference on Graphic and Image Processing (ICGIP 2014)
Yulin Wang; Xudong Jiang; David Zhang, Editor(s)

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