Title :
Design for Robustness Contour Detection CNN
Author :
Li, Guo-dong ; Zhao, Zhen-Yu ; Chen, De-gang ; Ye, Zhen-jun
Author_Institution :
Sch. of Math. & Phys., North China Electr. Power Univ., Beijing
Abstract :
The cellular neural nonlinear network (CNN) is a powerful tool for image and video signal processing, robotic and biological visions. This paper sets up a theorem to design robustness template CNN for contour detection in images, which provides parameter inequalities for determining parameter intervals for implementing the corresponding tasks. The contour CNN has successfully detected edges in three gray-scale images
Keywords :
cellular neural nets; edge detection; nonlinear functions; cellular neural nonlinear network; edge detection; gray-scale images; image contour detection; nonlinear function; parameter inequalities; parameter intervals; robustness template CNN design; Cellular neural networks; Cybernetics; Energy management; Gray-scale; Image edge detection; Machine learning; Mathematics; Object detection; Physics; Robot vision systems; Robustness; Roentgenium; Signal design; Cellular neural network; Contour detection; Gray-scale images; Template design;
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
DOI :
10.1109/ICMLC.2006.258633
