{"title":"一种有效的纹理图像分形维数计算方法","authors":"B. Chaudhuri, Nirupam Sarkar","doi":"10.1109/ICPR.1992.201575","DOIUrl":null,"url":null,"abstract":"Fractal dimension is a feature used to characterize roughness and self-similarity in a picture. This feature is used in texture segmentation and classification, shape analysis and other problems. An efficient differential box-counting approach to fractal dimension estimation is proposed and compared with four other methods.<<ETX>>","PeriodicalId":410961,"journal":{"name":"[1992] Proceedings. 11th IAPR International Conference on Pattern Recognition","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"An efficient approach to compute fractal dimension in texture image\",\"authors\":\"B. Chaudhuri, Nirupam Sarkar\",\"doi\":\"10.1109/ICPR.1992.201575\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Fractal dimension is a feature used to characterize roughness and self-similarity in a picture. This feature is used in texture segmentation and classification, shape analysis and other problems. An efficient differential box-counting approach to fractal dimension estimation is proposed and compared with four other methods.<<ETX>>\",\"PeriodicalId\":410961,\"journal\":{\"name\":\"[1992] Proceedings. 11th IAPR International Conference on Pattern Recognition\",\"volume\":\"66 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[1992] Proceedings. 11th IAPR International Conference on Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPR.1992.201575\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1992] Proceedings. 11th IAPR International Conference on Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPR.1992.201575","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An efficient approach to compute fractal dimension in texture image
Fractal dimension is a feature used to characterize roughness and self-similarity in a picture. This feature is used in texture segmentation and classification, shape analysis and other problems. An efficient differential box-counting approach to fractal dimension estimation is proposed and compared with four other methods.<>