{"title":"基于时复用CNN模拟器的噪声图像边缘检测","authors":"V. Murugesh, V. Arthy, V. Agalya","doi":"10.1109/ICCCNT.2014.6963142","DOIUrl":null,"url":null,"abstract":"This paper presents a cellular neural network based edge detection of noisy images using Time-Multiplexing CNN Simulator. The experimental results of Time-Multiplexing CNN Simulator are compared with traditional edge detection operators Canny and Sobel. Simulation results show that the proposed simulator is accurately detecting the edge of noisy images.","PeriodicalId":140744,"journal":{"name":"Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Edge detection of noisy images based on time-multiplexing CNN simulator\",\"authors\":\"V. Murugesh, V. Arthy, V. Agalya\",\"doi\":\"10.1109/ICCCNT.2014.6963142\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a cellular neural network based edge detection of noisy images using Time-Multiplexing CNN Simulator. The experimental results of Time-Multiplexing CNN Simulator are compared with traditional edge detection operators Canny and Sobel. Simulation results show that the proposed simulator is accurately detecting the edge of noisy images.\",\"PeriodicalId\":140744,\"journal\":{\"name\":\"Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT)\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCCNT.2014.6963142\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCNT.2014.6963142","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Edge detection of noisy images based on time-multiplexing CNN simulator
This paper presents a cellular neural network based edge detection of noisy images using Time-Multiplexing CNN Simulator. The experimental results of Time-Multiplexing CNN Simulator are compared with traditional edge detection operators Canny and Sobel. Simulation results show that the proposed simulator is accurately detecting the edge of noisy images.