Non-saturated binary image learning and recognition using the ratio-memory cellular neural network (RMCNN)

dc.citation.epage	629	en_US
dc.citation.spage	624	en_US
dc.contributor.author	Wu, CY	en_US
dc.contributor.author	Hsieh, CY	en_US
dc.contributor.author	Chen, SH	en_US
dc.contributor.author	Hsieh, BCY	en_US
dc.contributor.author	Chen, CR	en_US
dc.contributor.department	電子工程學系及電子研究所	zh_TW
dc.contributor.department	Department of Electronics Engineering and Institute of Electronics	en_US
dc.date.accessioned	2014-12-08T15:26:33Z
dc.date.available	2014-12-08T15:26:33Z
dc.date.issued	2002	en_US
dc.description.abstract	dIn this paper, cellular neural network with ratio memory is proposed for non-saturated binary image processing. The Hebbien learning rule will be used to learn the weight of template A. The RMCNN system can recognize one non-saturated binary image and remove most of the noise added to the image pattern during the recognition period. The behavior of recognizing non-saturated binary images will be proved by mathematics equations. The effect will be simulated by Matlab software. With the method for non-saturated binary image processing, this theory can be easily implemented in hardware.	en_US
dc.identifier.isbn	981-238-121-X	en_US
dc.identifier.journal	CELLULAR NEURAL NETWORKS AND THEIR APPLICATIONS	en_US
dc.identifier.uri	https://ir.lib.nycu.edu.tw/handle/11536/18845
dc.identifier.wosnumber	WOS:000178709500077
dc.language.iso	en_US	en_US
dc.title	Non-saturated binary image learning and recognition using the ratio-memory cellular neural network (RMCNN)	en_US
dc.type	Proceedings Paper	en_US

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