{"title":"基于二维半马尔可夫模型的边缘检测技术性能评价方法","authors":"D. Dubinin, V. Geringer, A. Kochegurov, K. Reif","doi":"10.1109/CICA.2014.7013248","DOIUrl":null,"url":null,"abstract":"The essay outlines one particular possibility of efficient evaluating the Performance of edge detector algorithms. Three generally known and published algorithms (Canny, Marr, Shen) were analysed by way of example. The analysis is based on two-dimensional signals created by means of two-dimensional Semi-Markov Model and subsequently provided with an additive Gaussian noise component. Five quality metrics allow an objective comparison of the algorithms.","PeriodicalId":340740,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"An efficient method to evaluate the performance of edge detection techniques by a two-dimensional Semi-Markov model\",\"authors\":\"D. Dubinin, V. Geringer, A. Kochegurov, K. Reif\",\"doi\":\"10.1109/CICA.2014.7013248\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The essay outlines one particular possibility of efficient evaluating the Performance of edge detector algorithms. Three generally known and published algorithms (Canny, Marr, Shen) were analysed by way of example. The analysis is based on two-dimensional signals created by means of two-dimensional Semi-Markov Model and subsequently provided with an additive Gaussian noise component. Five quality metrics allow an objective comparison of the algorithms.\",\"PeriodicalId\":340740,\"journal\":{\"name\":\"2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA)\",\"volume\":\"45 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CICA.2014.7013248\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CICA.2014.7013248","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An efficient method to evaluate the performance of edge detection techniques by a two-dimensional Semi-Markov model
The essay outlines one particular possibility of efficient evaluating the Performance of edge detector algorithms. Three generally known and published algorithms (Canny, Marr, Shen) were analysed by way of example. The analysis is based on two-dimensional signals created by means of two-dimensional Semi-Markov Model and subsequently provided with an additive Gaussian noise component. Five quality metrics allow an objective comparison of the algorithms.
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