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Proceedings Paper

Image segmentation based on double-level parallelized firing PCNN in complex environments
Author(s): Biao Jiang; Zhenming Peng; Jun Xiao; Hongbing Wang
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Paper Abstract

A novel method for image segmentation using double-level parallelized firing pulse coupled neural networks (DLPFPCNN) is presented in this paper. The first level (or auxiliary level) is used to enhance image by improved and simplified PCNN model combining with boundary enhancement, which can give the better results for the second level (or primary level) PCNN. The primary level uses a parallelized firing PCNN (PFPCNN) model to segment the enhanced images so that can improve the adaptability to the complex environment. Parallelized firing neuron model can overcome the drawbacks for sequential pulse burst, which is unfair for those pixels at low grayscale value areas. Finally, the optimal segmentation results are determined by maximum Shannon entropy of image. Experimental results show, as compared to the conventional PCNN model with single level and sequential pulse burst, the proposed method can improve the performance of image segmentation and obtain the good results, especially suiting for those images with low contrast, low signal-to-noise ratio (SNR) and continuously spatial-varying background.

Paper Details

Date Published: 29 November 2007
PDF: 10 pages
Proc. SPIE 6833, Electronic Imaging and Multimedia Technology V, 68331Z (29 November 2007); doi: 10.1117/12.755274
Show Author Affiliations
Biao Jiang, Univ. of Electronic Science and Technology of China (China)
Zhenming Peng, Univ. of Electronic Science and Technology of China (China)
Jun Xiao, Univ. of Electronic Science and Technology of China (China)
Hongbing Wang, Univ. of Electronic Science and Technology of China (China)

Published in SPIE Proceedings Vol. 6833:
Electronic Imaging and Multimedia Technology V
Liwei Zhou; Chung-Sheng Li; Minerva M. Yeung, Editor(s)

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