29 November 2007 Image segmentation based on double-level parallelized firing PCNN in complex environments
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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.
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Biao Jiang, Zhenming Peng, Jun Xiao, Hongbing Wang, "Image segmentation based on double-level parallelized firing PCNN in complex environments", Proc. SPIE 6833, Electronic Imaging and Multimedia Technology V, 68331Z (29 November 2007); doi: 10.1117/12.755274; https://doi.org/10.1117/12.755274
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