@inproceedings{cd4a82d60d474eb88aa2ca7ccf82c3ea,

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

abstract = "In 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 ofthe 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.",

keywords = "Capacitors, Cellular neural networks, Equations, Image analysis, Image processing, Image recognition, Leakage current, Mathematics, Very large scale integration",

author = "Chung-Yu Wu and Hsieh, {Chieh Yu} and Chen, {Sheng Hao} and Hsieh, {Brian Che Yuan} and Chen, {Cheng Ruei}",

year = "2002",

month = jan,

day = "1",

doi = "10.1109/CNNA.2002.1035104",

language = "English",

series = "Proceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "624--629",

editor = "Ronald Tetzlaff",

booktitle = "Proceedings of the 7th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2002",

address = "United States",

note = "null ; Conference date: 22-07-2002 Through 24-07-2002",

}