{"title":"调制域纹理分解","authors":"C. T. Nguyen, J. Havlicek","doi":"10.1109/ICIP.2010.5651542","DOIUrl":null,"url":null,"abstract":"We introduce a novel texture analysis algorithm capable of extracting visually meaningful and locally coherent sub-textural components from images. The algorithm operates in the modulation domain where texture is represented by locally coherent amplitude and frequency modulation functions. The texture components are iteratively extracted based on a new quantitative coherency measure. The effectiveness of the algorithm is demonstrated on several well-known Brodatz textures.","PeriodicalId":228308,"journal":{"name":"2010 IEEE International Conference on Image Processing","volume":"21 1-3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Modulation domain texture decomposition\",\"authors\":\"C. T. Nguyen, J. Havlicek\",\"doi\":\"10.1109/ICIP.2010.5651542\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We introduce a novel texture analysis algorithm capable of extracting visually meaningful and locally coherent sub-textural components from images. The algorithm operates in the modulation domain where texture is represented by locally coherent amplitude and frequency modulation functions. The texture components are iteratively extracted based on a new quantitative coherency measure. The effectiveness of the algorithm is demonstrated on several well-known Brodatz textures.\",\"PeriodicalId\":228308,\"journal\":{\"name\":\"2010 IEEE International Conference on Image Processing\",\"volume\":\"21 1-3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-12-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE International Conference on Image Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIP.2010.5651542\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE International Conference on Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIP.2010.5651542","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We introduce a novel texture analysis algorithm capable of extracting visually meaningful and locally coherent sub-textural components from images. The algorithm operates in the modulation domain where texture is represented by locally coherent amplitude and frequency modulation functions. The texture components are iteratively extracted based on a new quantitative coherency measure. The effectiveness of the algorithm is demonstrated on several well-known Brodatz textures.