{"title":"一种用于边缘检测的诺贝尔混合方法","authors":"Palvi Rani, Poonam Tanwar","doi":"10.5121/IJCSES.2013.4203","DOIUrl":null,"url":null,"abstract":"The objective of this paper is to present the hybrid approach for edge detection. Under this technique, edge detection is performed in two phase. In first phase, Canny Algorithm is applied for image smoothing and in second phase neural network is to detecting actual edges. Neural network is a wonderful tool for edge detection. As it is a non-linear network with built-in thresholding capability. Neural Network can be trained with back propagation technique using few training patterns but the most important and difficult part is to identify the correct and proper training set.","PeriodicalId":415526,"journal":{"name":"International Journal of Computer Science & Engineering Survey","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"A NOBEL HYBRID APPROACH FOR EDGE DETECTION\",\"authors\":\"Palvi Rani, Poonam Tanwar\",\"doi\":\"10.5121/IJCSES.2013.4203\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The objective of this paper is to present the hybrid approach for edge detection. Under this technique, edge detection is performed in two phase. In first phase, Canny Algorithm is applied for image smoothing and in second phase neural network is to detecting actual edges. Neural network is a wonderful tool for edge detection. As it is a non-linear network with built-in thresholding capability. Neural Network can be trained with back propagation technique using few training patterns but the most important and difficult part is to identify the correct and proper training set.\",\"PeriodicalId\":415526,\"journal\":{\"name\":\"International Journal of Computer Science & Engineering Survey\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-04-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Computer Science & Engineering Survey\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5121/IJCSES.2013.4203\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Computer Science & Engineering Survey","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5121/IJCSES.2013.4203","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The objective of this paper is to present the hybrid approach for edge detection. Under this technique, edge detection is performed in two phase. In first phase, Canny Algorithm is applied for image smoothing and in second phase neural network is to detecting actual edges. Neural network is a wonderful tool for edge detection. As it is a non-linear network with built-in thresholding capability. Neural Network can be trained with back propagation technique using few training patterns but the most important and difficult part is to identify the correct and proper training set.