{"title":"热图像中不同平均亮度类数的非参数估计","authors":"A.N. Galyntich, M.A. Raifeld","doi":"10.18287/2412-6179-co-1284","DOIUrl":null,"url":null,"abstract":"When there is no information about the number of brightness classes, synthesizing algorithms for automatic image threshold segmentation involves a problem of determining the number of thresholds. The solution to the problem of estimating the number of classes in an image can be based on representing its distribution as a mixture of distributions of brightness classes when priori probabilities are unknown, or estimating the number of histogram modes. At the same time, it is known that the mixture splitting problem has a solution only for certain types of distributions and the histogram modes are not always distinguishable. In the general case, when the distributions of brightness classes are unknown, there are difficulties in applying these methods. The article proposes a non-parametric approach to determining the number of classes that differ in average brightness, based on rank histograms and using the property of local spatial grouping of elements of each brightness class in the image.","PeriodicalId":46692,"journal":{"name":"Computer Optics","volume":"42 1","pages":"0"},"PeriodicalIF":1.1000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Nonparametric estimation of the number of classes with different average brightness in thermal images\",\"authors\":\"A.N. Galyntich, M.A. Raifeld\",\"doi\":\"10.18287/2412-6179-co-1284\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"When there is no information about the number of brightness classes, synthesizing algorithms for automatic image threshold segmentation involves a problem of determining the number of thresholds. The solution to the problem of estimating the number of classes in an image can be based on representing its distribution as a mixture of distributions of brightness classes when priori probabilities are unknown, or estimating the number of histogram modes. At the same time, it is known that the mixture splitting problem has a solution only for certain types of distributions and the histogram modes are not always distinguishable. In the general case, when the distributions of brightness classes are unknown, there are difficulties in applying these methods. The article proposes a non-parametric approach to determining the number of classes that differ in average brightness, based on rank histograms and using the property of local spatial grouping of elements of each brightness class in the image.\",\"PeriodicalId\":46692,\"journal\":{\"name\":\"Computer Optics\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2023-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Optics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18287/2412-6179-co-1284\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"OPTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Optics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18287/2412-6179-co-1284","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"OPTICS","Score":null,"Total":0}
Nonparametric estimation of the number of classes with different average brightness in thermal images
When there is no information about the number of brightness classes, synthesizing algorithms for automatic image threshold segmentation involves a problem of determining the number of thresholds. The solution to the problem of estimating the number of classes in an image can be based on representing its distribution as a mixture of distributions of brightness classes when priori probabilities are unknown, or estimating the number of histogram modes. At the same time, it is known that the mixture splitting problem has a solution only for certain types of distributions and the histogram modes are not always distinguishable. In the general case, when the distributions of brightness classes are unknown, there are difficulties in applying these methods. The article proposes a non-parametric approach to determining the number of classes that differ in average brightness, based on rank histograms and using the property of local spatial grouping of elements of each brightness class in the image.
期刊介绍:
The journal is intended for researchers and specialists active in the following research areas: Diffractive Optics; Information Optical Technology; Nanophotonics and Optics of Nanostructures; Image Analysis & Understanding; Information Coding & Security; Earth Remote Sensing Technologies; Hyperspectral Data Analysis; Numerical Methods for Optics and Image Processing; Intelligent Video Analysis. The journal "Computer Optics" has been published since 1987. Published 6 issues per year.