Image segmentation algorithm based on improved PCNN

被引：0

作者：

Chen Hong ^{[1
,2
]}

Wu Chengdong ^{[1
]}

Yu Xiaosheng ^{[1
]}

Wu Jiahui ^{[1
]}

机构：

[1] Northeastern Univ, Shenyang 110819, Liaoning, Peoples R China

[2] Anshan Normal Univ, Anshan 114005, Peoples R China

来源：

LIDAR IMAGING DETECTION AND TARGET RECOGNITION 2017 | 2017年 / 10605卷

基金：

美国国家科学基金会;

关键词：

PCNN; Image segmentation; Local information entropy; PSO;

D O I：

10.1117/12.2292798

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

A modified simplified Pulse Coupled Neural Network (PCNN) model is proposed in this article based on simplified PCNN. Some work have done to enrich this model, such as imposing restrictions items of the inputs, improving linking inputs and internal activity of PCNN. A self-adaptive parameter setting method of linking coefficient and threshold value decay time constant is proposed here, too. At last, we realized image segmentation algorithm for five pictures based on this proposed simplified PCNN model and PSO. Experimental results demonstrate that this image segmentation algorithm is much better than method of SPCNN and OTSU.

引用

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页数：6

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