{"title":"基于统计变化点检测的最优全局阈值估计","authors":"R. Chatterjee, A. Kar","doi":"10.5121/SIPIJ.2017.8402","DOIUrl":null,"url":null,"abstract":"Aim of this paper is reformulation of global image thresholding problem as a well-founded statistical method known as change-point detection (CPD) problem. Our proposed CPD thresholding algorithm does not assume any prior statistical distribution of background and object grey levels. Further, this method is less influenced by an outlier due to our judicious derivation of a robust criterion function depending on Kullback-Leibler (KL) divergence measure. Experimental result shows efficacy of proposed method compared to other popular methods available for global image thresholding. In this paper we also propose a performance criterion for comparison of thresholding algorithms. This performance criteria does not depend on any ground truth image. We have used this performance criterion to compare the results of proposed thresholding algorithm with most cited global thresholding algorithms in the literature.","PeriodicalId":90726,"journal":{"name":"Signal and image processing : an international journal","volume":"9 1","pages":"15-24"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Optimal Global Threshold Estimation Using Statistical Change Point Detection\",\"authors\":\"R. Chatterjee, A. Kar\",\"doi\":\"10.5121/SIPIJ.2017.8402\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aim of this paper is reformulation of global image thresholding problem as a well-founded statistical method known as change-point detection (CPD) problem. Our proposed CPD thresholding algorithm does not assume any prior statistical distribution of background and object grey levels. Further, this method is less influenced by an outlier due to our judicious derivation of a robust criterion function depending on Kullback-Leibler (KL) divergence measure. Experimental result shows efficacy of proposed method compared to other popular methods available for global image thresholding. In this paper we also propose a performance criterion for comparison of thresholding algorithms. This performance criteria does not depend on any ground truth image. We have used this performance criterion to compare the results of proposed thresholding algorithm with most cited global thresholding algorithms in the literature.\",\"PeriodicalId\":90726,\"journal\":{\"name\":\"Signal and image processing : an international journal\",\"volume\":\"9 1\",\"pages\":\"15-24\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Signal and image processing : an international journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5121/SIPIJ.2017.8402\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Signal and image processing : an international journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5121/SIPIJ.2017.8402","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimal Global Threshold Estimation Using Statistical Change Point Detection
Aim of this paper is reformulation of global image thresholding problem as a well-founded statistical method known as change-point detection (CPD) problem. Our proposed CPD thresholding algorithm does not assume any prior statistical distribution of background and object grey levels. Further, this method is less influenced by an outlier due to our judicious derivation of a robust criterion function depending on Kullback-Leibler (KL) divergence measure. Experimental result shows efficacy of proposed method compared to other popular methods available for global image thresholding. In this paper we also propose a performance criterion for comparison of thresholding algorithms. This performance criteria does not depend on any ground truth image. We have used this performance criterion to compare the results of proposed thresholding algorithm with most cited global thresholding algorithms in the literature.