{"title":"基于高斯的边缘检测方法综述","authors":"M. Basu","doi":"10.1109/TSMCC.2002.804448","DOIUrl":null,"url":null,"abstract":"The Gaussian filter has been used extensively in image processing and computer vision for many years. We discuss the various features of this operator that make it the filter of choice in the area of edge detection. Despite these desirable features of the Gaussian filter, edge detection algorithms which use it suffer from many problems. We review several linear and nonlinear Gaussian-based edge detection methods.","PeriodicalId":55005,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Re","volume":"24 13","pages":"252-260"},"PeriodicalIF":0.0000,"publicationDate":"2002-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"526","resultStr":"{\"title\":\"Gaussian-based edge-detection methods - a survey\",\"authors\":\"M. Basu\",\"doi\":\"10.1109/TSMCC.2002.804448\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Gaussian filter has been used extensively in image processing and computer vision for many years. We discuss the various features of this operator that make it the filter of choice in the area of edge detection. Despite these desirable features of the Gaussian filter, edge detection algorithms which use it suffer from many problems. We review several linear and nonlinear Gaussian-based edge detection methods.\",\"PeriodicalId\":55005,\"journal\":{\"name\":\"IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Re\",\"volume\":\"24 13\",\"pages\":\"252-260\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"526\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Re\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TSMCC.2002.804448\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Re","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TSMCC.2002.804448","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Gaussian filter has been used extensively in image processing and computer vision for many years. We discuss the various features of this operator that make it the filter of choice in the area of edge detection. Despite these desirable features of the Gaussian filter, edge detection algorithms which use it suffer from many problems. We review several linear and nonlinear Gaussian-based edge detection methods.