DocumentCode
2897915
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
fYear
2006
fDate
13-16 Aug. 2006
Firstpage
3721
Lastpage
3724
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location
Dalian, China
Print_ISBN
1-4244-0061-9
Type
conf
DOI
10.1109/ICMLC.2006.258633
Filename
4028717
Link To Document