Robust design of dilation and erosion CNN for gray scale image

The cellular neural/nonlinear network (CNN) is a powerful tool for image and video signal processing, as well as robotic and biological visions. The designs for CNN templates are one of the important issues for the practical applications of CNNs. This paper combines two CNN to implement the Dilation CNNs and Erosion CNN for gray scale images and proposes two theorems of robustness designs. The parameters of the templates can range between a hyper plane and a hyper surface in the first quartile. The simulations have been given. The results show the effectiveness of the theoretical results to be implemented in computer simulations.

[1]  Luigi Fortuna,et al.  Image processing for medical diagnosis using CNN , 2003 .

[2]  Lequan Min,et al.  Robust design of bipolar wave cellular neural network with applications , 2010, Int. J. Model. Identif. Control..

[3]  Lequan Min,et al.  Robust Designs for Templates of Directional Extraction Cellular Neural Network with Application , 2007 .

[4]  Lequan Min,et al.  Robust Designs for Templates of Directional Extraction Cellular Neural Network with Application , 2007, 2007 International Conference on Computational Intelligence and Security Workshops (CISW 2007).

[5]  Lequan Min,et al.  Robustness Design of Templates of Directed Overstrike CNNs with Applications , 2004 .

[6]  THE ANALOGIC CELLULAR NEURAL NETWORK , 2016 .

[7]  Osman N. Ucan,et al.  Application of cellular neural network (CNN) to the prediction of missing air pollutant data , 2011 .

[8]  Leon O. Chua,et al.  Cellular neural networks: applications , 1988 .

[9]  Lequan Min,et al.  Robust Designs for Gray-Scale Global Connectivity Detection CNN Templates , 2007, Int. J. Bifurc. Chaos.

[10]  Min Lequan,et al.  Design for CNN Templates with Performance of Global Connectivity Detection , 2004 .

[11]  Klaus Mainzer,et al.  Cellular Neural Networks and Visual Computing , 2003, Int. J. Bifurc. Chaos.

[12]  Lin Chen,et al.  Robust Designs of Selected Objects Extraction CNN , 2009, 2009 2nd International Congress on Image and Signal Processing.

[13]  Leon O. Chua,et al.  CNN: A Vision of Complexity , 1997 .

[14]  Leon O. Chua,et al.  UNIVERSAL CNN CELLS , 1999 .

[16]  Lin-Bao Yang,et al.  Cellular neural networks: theory , 1988 .

[17]  Lequan Min,et al.  Two Theorems on the Robust Designs for Dilation and Erosion CNNs , 2007, 2007 International Conference on Communications, Circuits and Systems.

[18]  X. Liao,et al.  Edge detection of noisy images based on cellular neural networks , 2011 .

[19]  Yuan Tian,et al.  Application of new advanced CNN structure with adaptive thresholds to color edge detection , 2012 .

[20]  G.S. Moschytz,et al.  Genetic optimization of cellular neural networks , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[21]  Lequan Min,et al.  Color Edge Detections Based on Cellular Neural Network , 2008, Int. J. Bifurc. Chaos.

[22]  LIUJin-Zhu,et al.  Design for CNN Templates with Performance of Global Connectivity Detection , 2004 .

[23]  Lequan Min,et al.  Dynamic Analysis of Coupled Binary Stripe CNNs , 2011, 2011 International Conference on Intelligent Computation and Bio-Medical Instrumentation.