Edge detection of infrared image with CNN_DGA algorithm

Abstract In this paper, infrared image edge detection algorithm based on the combination of the cellular neural networks (CNN) and distributed genetic algorithm (DGA) is proposed. The CNN template is used to train the network with distributed genetic algorithm (CNN_DGA). The experimental results show: the edge of the infrared image was extracted accurately with CNN_DGA edge detection algorithm; furthermore, the noise of the infrared image was reduced greatly. Compared with the edge detection algorithms based on cellular neural networks with template trained by particle swarm optimization, parameters’ search range and convergence speed are greatly improved.

[1]  Yan Jie.  An Algorithm of Infrared Imaging Edge Detection Based on Grey Relational Analysis , 2006 .

[2]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[3]  Chao Sun,et al.  A Novel Algorithm for Edge Detection of Remote Sensing Image Based on CNN and PSO , 2009, 2009 2nd International Congress on Image and Signal Processing.

[4]  Tomoyuki Hiroyasu,et al.  Distributed genetic algorithms with randomized migration rate , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[5]  P. Lopez,et al.  Design and training of multilayer discrete time cellular neural networks for antipersonnel mine detection using genetic algorithms , 2000, Proceedings of the 2000 6th IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA 2000) (Cat. No.00TH8509).

[6]  Erik D. Goodman,et al.  Designing templates for cellular neural networks using particle swarm optimization , 2004, 33rd Applied Imagery Pattern Recognition Workshop (AIPR'04).

[7]  Josef A. Nossek,et al.  Cellular neural network design using a learning algorithm , 1990, IEEE International Workshop on Cellular Neural Networks and their Applications.

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

[9]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

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

[11]  A. Basturk,et al.  Clonal selection algorithm based cloning template learning for edge detection in digital images with CNN , 2008, 2008 IEEE 16th Signal Processing, Communication and Applications Conference.

[12]  Chin-Teng Lin,et al.  Image descreening by GA-CNN-based texture classification , 2004, IEEE Trans. Circuits Syst. I Regul. Pap..

[13]  Diego Cabello,et al.  Design of multilayer discrete time cellular neural networks for image processing tasks based on genetic algorithms , 2000, 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353).