一种参数自适应的简化PCNN图像分割方法

周东国; 高潮; 郭永彩

doi:10.3724/SP.J.1004.2014.01191

一种参数自适应的简化PCNN图像分割方法

doi: 10.3724/SP.J.1004.2014.01191

1.
重庆大学光电工程学院光电技术及系统教育部重点实验室重庆 400030

基金项目:

教育部博士点基金（20130191110021），中央高校基本科研业务费科研专项研究生科研创新基金（CDJXS11120022）资助

详细信息

作者简介:
高潮重庆大学光电工程学院教授，博士. 主要研究方向为电子技术，计算机信息处理及精密测控.E-mail：gaoc@cqu.edu.cn

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- 被引次数: 0
出版历程
- 收稿日期: 2013-03-07
- 修回日期: 2013-10-25
- 刊出日期: 2014-06-20

Adaptive Simplified PCNN Parameter Setting for Image Segmentation

1.
Key Laboratory of Optoelectronic Technology and Systems of Ministry of Education, College of Opto-electronic Engineering, Chongqing 400030

Funds:

Supported by Ph.D. Programs Foundation of Ministry of Education of China (20130191110021) and Fundamental Research Funds for the Central Universities (CDJXS11120022)

摘要

摘要: 为了进一步延伸脉冲耦合神经网络（Pulse coupled neural network，PCNN）在图像分割中的应用，本文对PCNN模型作了简化和改进，并探讨和分析了参数的设置方法.首先利用阈值和脉冲输出所对应的区域均值之间的关系，提出了一种优化连接系数的方法，使得模型最终以迭代的方式得到分割结果.在仿真和真实红外图像上实验结果表明，文中方法能取得较优的分割效果，且相比于常用的阈值方法以及较新的PCNN方法，文中的简化模型对噪声及复杂图像具有更好的适应性和鲁棒性.
- 脉冲耦合神经网络 /
- 红外图像分割 /
- 阈值 /
- 连接系数
Abstract: In order to extend the pulse coupled neural network (PCNN) to image segmentation, this paper improves the original PCNN model and simplifies its parameters, and then presents a method for adaptive parameter settings. The proposed method adopts the relationship between the neural threshold and the region characteristic of pulse output. Meanwhile, an optimal method is introduced to find the optimal linking coefficient value. As a result, our model can iteratively segments the targets from the background. Experimental results on synthetic and real infrared images demonstrate that the proposed method has better adaptability to the complicated images when compared with the widely used threshold and two state-of-the-art PCNN models, and shows good robustness for noisy images.
- Pulse coupled neural network (PCNN) /
- infrared image segmentation /
- threshold /
- linking coefficient

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参考文献(22)

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