The cellular neural/nonlinear network （CNN） is a powerful tool for image and video signal processing, robotic and biological visions. This paper introduces a kind of CNNs with performance of extracting closed domain...The cellular neural/nonlinear network （CNN） is a powerful tool for image and video signal processing, robotic and biological visions. This paper introduces a kind of CNNs with performance of extracting closed domains in binary images, and gives a general method for designing templates of such a kind of CNNs. One theorem provides parameter inequalities for determining parameter intervals for implementing prescribed image processing functions, respectively. Examples for extracting closed domains in binary scale images are given.展开更多
基金The project supported by National Natural Science Foundation of China under Grant No. 70271068, the Foundation for University Key Teachers, and the Research Fund for the Doctoral Program of Higher Education of the Ministry of Education of China under Grant No. 200200080004
文摘The cellular neural/nonlinear network （CNN） is a powerful tool for image and video signal processing, robotic and biological visions. This paper introduces a kind of CNNs with performance of extracting closed domains in binary images, and gives a general method for designing templates of such a kind of CNNs. One theorem provides parameter inequalities for determining parameter intervals for implementing prescribed image processing functions, respectively. Examples for extracting closed domains in binary scale images are given.
