基于比例记忆细胞神经网络的非饱和二值图像学习与识别

Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications Pub Date : 2002-07-24 DOI:10.1109/CNNA.2002.1035104

Chung-Yu Wu, Chieh-Yu Hsieh, Sheng-Hao Chen, B. C. Hsieh, Cheng-Ruei Chen

{"title":"基于比例记忆细胞神经网络的非饱和二值图像学习与识别","authors":"Chung-Yu Wu, Chieh-Yu Hsieh, Sheng-Hao Chen, B. C. Hsieh, Cheng-Ruei Chen","doi":"10.1109/CNNA.2002.1035104","DOIUrl":null,"url":null,"abstract":"In this paper, cellular neural network with ratio memory is proposed for non-saturated binary image processing. The Hebbien leaming lule will be used to leam the weight oftemplate A. The RMCNN system can recognize one non-saNmted binary image and remove most ofthe noise added to the image pattem 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 sothare. With the method for non-SaNrated binarylmage processing, this theory can be easily implemented in hardware.","PeriodicalId":387716,"journal":{"name":"Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2002-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Non-saturated binary image learning and recognition using the ratio-memory cellular neural network (RMCNN)\",\"authors\":\"Chung-Yu Wu, Chieh-Yu Hsieh, Sheng-Hao Chen, B. C. Hsieh, Cheng-Ruei Chen\",\"doi\":\"10.1109/CNNA.2002.1035104\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, cellular neural network with ratio memory is proposed for non-saturated binary image processing. The Hebbien leaming lule will be used to leam the weight oftemplate A. The RMCNN system can recognize one non-saNmted binary image and remove most ofthe noise added to the image pattem 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 sothare. With the method for non-SaNrated binarylmage processing, this theory can be easily implemented in hardware.\",\"PeriodicalId\":387716,\"journal\":{\"name\":\"Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-07-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CNNA.2002.1035104\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CNNA.2002.1035104","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 2

摘要

本文提出了一种带比率记忆的细胞神经网络用于非饱和二值图像处理。采用Hebbien学习规则学习模板a的权值。RMCNN系统可以识别一幅未被分割的二值图像，并在识别过程中去除图像模式中添加的大部分噪声。用数学方程证明了非饱和二值图像的识别行为。用Matlab软件对其效果进行仿真。利用非分级二进制图像处理方法，该理论可以很容易地在硬件上实现。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

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

In this paper, cellular neural network with ratio memory is proposed for non-saturated binary image processing. The Hebbien leaming lule will be used to leam the weight oftemplate A. The RMCNN system can recognize one non-saNmted binary image and remove most ofthe noise added to the image pattem 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 sothare. With the method for non-SaNrated binarylmage processing, this theory can be easily implemented in hardware.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications

自引率

0.00%

发文量