DocumentCode
2560445
Title
Robustness designs of a kind of uncoupled CNNs with applications
Author
Min, Lequan
Author_Institution
Dept. of Math. & Mech., Beijing Univ. of Sci. & Technol., China
fYear
2005
fDate
28-30 May 2005
Firstpage
98
Lastpage
101
Abstract
The cellular neural/nonlinear network (CNN) is a powerful tool for image and video signal processing, robotic and biological visions, and higher brain functions. This paper discusses a general method for robustness designs of a kind of uncoupled CNNs. Two theorems provide parameter inequalities for determining parameter intervals for implementing prescribed image processing functions, respectively. Examples for detecting edges and corners in gray scale images are given.
Keywords
cellular neural nets; image processing; stability; cellular neural nonlinear network; image processing; parameter inequalities; robustness designs; uncoupled CNN; Cellular networks; Cellular neural networks; Design methodology; Image edge detection; Image processing; Mathematics; Robot vision systems; Robustness; Signal design; Video signal processing; binary and gray scale image processing; cellular neural network; robustness template design;
fLanguage
English
Publisher
ieee
Conference_Titel
Cellular Neural Networks and Their Applications, 2005 9th International Workshop on
Print_ISBN
0-7803-9185-3
Type
conf
DOI
10.1109/CNNA.2005.1543170
Filename
1543170
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