A New Image Segmentation Algorithm Based on PCNN and Maximal Correlative Criterion

摘要：

Pulse Coupled Neural Network (PCNN) is a new generation of artificial neural networks, which has biological background, embodies excellent performance in image segmentation. However, the problem of parameter estimation and threshold iteration in PCNN model has not been resolved yet. This paper combined 1-dimensional Maximal Correlative Criterion with 2-dimensional Maximal Correlative Criterion to estimate neuron parameters, achieved the automation of image segmentation and reduced the complexity of computing. Simulation results showed that the algorithm has prominent improvement in image segmentation effect and computing complexity and has general applicability compared to relevant literatures.

关键词： image segmentation PCNN Maximal correlative Criterion

作者: Wang Xinchun Ye Qing Yue Kaihu Liu Ruiming Shu Kangyun

作者单位: Department of Physical and Electronics, Chuxiong Normal University, Chuxiong, China

会议类型: 国际会议

会议名称: 2010 IEEE 10th International Conference on Signal Processing(第十届信号处理国际会议 ICSP 2010)

会议地点: 北京

会议语种:英文

页码: 873-876

在线出版日期: 2010-08-24（万方平台首次上网日期，不代表论文的发表时间）