{"title":"A robust edge indicator employing nonlocal structure tensor","authors":"Xianghua Tan, Tao Tang","doi":"10.1109/FSKD.2016.7603416","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a robust edge indicator employing two eigenvalues of nonlocal structure tensor matrix. In our method, a new nonlocal structure tensor is first constructed. This structure tensor is robust to noise, which inherits from nonlocal means algorithm. Furthermore, based on the constructed nonlocal structure tensor, a new and edge indicator is built, which can effectively differentiate a pixel at edge from a pixel in flat region with noise. Moreover, the proposed edge indicator can characteristic the strength of the edges. By experiments, the results of our method is superior to the gradient's results.","PeriodicalId":373155,"journal":{"name":"2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"139 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FSKD.2016.7603416","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, we propose a robust edge indicator employing two eigenvalues of nonlocal structure tensor matrix. In our method, a new nonlocal structure tensor is first constructed. This structure tensor is robust to noise, which inherits from nonlocal means algorithm. Furthermore, based on the constructed nonlocal structure tensor, a new and edge indicator is built, which can effectively differentiate a pixel at edge from a pixel in flat region with noise. Moreover, the proposed edge indicator can characteristic the strength of the edges. By experiments, the results of our method is superior to the gradient's results.