Two Theorems on the Robust Designs for Dilation and Erosion CNNs

摘要：

The cellular neural/nonlinear network (CNN) has become a new tool for image and signal processing, robotic and biological visions, and higher brain functions. Based our previous research, this paper set up two new theorems of robust designs for Dilation and Erosion CNNs processing gray-scale images, which provide parameter inequalities to determine parameter intervals for implementing prescribed image processing functions, respectively. Four numerical simulation examples for Dilation and Erosion CNNs are given to illustrate the effectiveness of our theorems.

作者: Shu Jian Bing Zhao Lequan Min

作者单位: Applied Science School, University of Science and Technology Beijing Beijing 100083, PR China Applied Science School and Information School, University of Science and Technology Beijing Beijing

会议类型: 国际会议

会议名称: 2007年通信、电路与系统国际会议(2007 International Conference on Communications,Circuits and Systems Proceedings)

会议地点: 日本福冈

会议语种:英文

在线出版日期: 2007-07-11（万方平台首次上网日期，不代表论文的发表时间）