{"title":"一种基于细胞神经网络的灰度图像边缘检测方法","authors":"J. A. Hernández, F. G. Castañeda, J. Cadenas","doi":"10.1109/MWSCAS.2009.5235993","DOIUrl":null,"url":null,"abstract":"Edge detection is an important preprocessing task in artificial vision systems. In this paper the utility of a recently reported CNN template for edge detection was verified over a set of black and white images. These images were obtained applying an threshold procedure to their corresponding associated gray level images. An optimal threshold value for preserving a large number of features from the original gray level input images was used. Combining the threshold and edge detection templates, a procedure to obtain edges on gray level images was implemented.","PeriodicalId":254577,"journal":{"name":"2009 52nd IEEE International Midwest Symposium on Circuits and Systems","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A method for edge detection in gray level images, based on cellular neural networks\",\"authors\":\"J. A. Hernández, F. G. Castañeda, J. Cadenas\",\"doi\":\"10.1109/MWSCAS.2009.5235993\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Edge detection is an important preprocessing task in artificial vision systems. In this paper the utility of a recently reported CNN template for edge detection was verified over a set of black and white images. These images were obtained applying an threshold procedure to their corresponding associated gray level images. An optimal threshold value for preserving a large number of features from the original gray level input images was used. Combining the threshold and edge detection templates, a procedure to obtain edges on gray level images was implemented.\",\"PeriodicalId\":254577,\"journal\":{\"name\":\"2009 52nd IEEE International Midwest Symposium on Circuits and Systems\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-09-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 52nd IEEE International Midwest Symposium on Circuits and Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MWSCAS.2009.5235993\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 52nd IEEE International Midwest Symposium on Circuits and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MWSCAS.2009.5235993","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A method for edge detection in gray level images, based on cellular neural networks
Edge detection is an important preprocessing task in artificial vision systems. In this paper the utility of a recently reported CNN template for edge detection was verified over a set of black and white images. These images were obtained applying an threshold procedure to their corresponding associated gray level images. An optimal threshold value for preserving a large number of features from the original gray level input images was used. Combining the threshold and edge detection templates, a procedure to obtain edges on gray level images was implemented.
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