DocumentCode :
642673
Title :
Applying Cellular Neural Networks dynamics for image representation
Author :
Tang Tang ; Tetzlaff, Ronald
Author_Institution :
Fac. of Electr. Eng & Inf. Technol., Tech. Univ. Dresden, Dresden, Germany
fYear :
2013
fDate :
8-12 Sept. 2013
Firstpage :
1
Lastpage :
4
Abstract :
In this paper we discuss in detail the feasibility of implementation and realization of uncoupled Cellular Neural Networks (CNN) systems for image representation. Applying CNN systems for representation of binary image patterns with sparse distribution of points as an example for a possible application is studied here. The test results show a high quality of representation with this method and proved it to be a possible way to implement the proposed CNN structures in practical application.
Keywords :
cellular neural nets; image representation; CNN structures; CNN systems; binary image patterns; cellular neural networks dynamics; image representation; sparse distribution; Accuracy; Hamming distance; Image coding; Image representation; Polynomials; Standards; Wavelet coefficients; CNN; Nonlinear dynamical systems; image representation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Circuit Theory and Design (ECCTD), 2013 European Conference on
Conference_Location :
Dresden
Type :
conf
DOI :
10.1109/ECCTD.2013.6662224
Filename :
6662224
Link To Document :
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