Title :
Robustness designs of a kind of uncoupled CNNs with applications
Author_Institution :
Dept. of Math. & Mech., Beijing Univ. of Sci. & Technol., China
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;
Conference_Titel :
Cellular Neural Networks and Their Applications, 2005 9th International Workshop on
Print_ISBN :
0-7803-9185-3
DOI :
10.1109/CNNA.2005.1543170
