{"title":"独立样本和相关样本分形维数的极大似然估计分析","authors":"A. Parshin, Y. Parshin","doi":"10.1109/MECO.2016.7525732","DOIUrl":null,"url":null,"abstract":"Paper describes algorithms of the fractal dimension estimation. Their effectiveness is considered in condition of independent and dependent samples of vectors in pseudophase space. Expression for the correlation dimension estimate for dependent samples is calculated. Ways of sample ordering are considered. Appliance of these algorithms for textural processing is proposed.","PeriodicalId":253666,"journal":{"name":"2016 5th Mediterranean Conference on Embedded Computing (MECO)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Analysis of maximum likelihood estimation of fractal dimension by independent and dependent samples\",\"authors\":\"A. Parshin, Y. Parshin\",\"doi\":\"10.1109/MECO.2016.7525732\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Paper describes algorithms of the fractal dimension estimation. Their effectiveness is considered in condition of independent and dependent samples of vectors in pseudophase space. Expression for the correlation dimension estimate for dependent samples is calculated. Ways of sample ordering are considered. Appliance of these algorithms for textural processing is proposed.\",\"PeriodicalId\":253666,\"journal\":{\"name\":\"2016 5th Mediterranean Conference on Embedded Computing (MECO)\",\"volume\":\"44 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-06-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 5th Mediterranean Conference on Embedded Computing (MECO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MECO.2016.7525732\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 5th Mediterranean Conference on Embedded Computing (MECO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MECO.2016.7525732","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Analysis of maximum likelihood estimation of fractal dimension by independent and dependent samples
Paper describes algorithms of the fractal dimension estimation. Their effectiveness is considered in condition of independent and dependent samples of vectors in pseudophase space. Expression for the correlation dimension estimate for dependent samples is calculated. Ways of sample ordering are considered. Appliance of these algorithms for textural processing is proposed.
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