{"title":"基于二维假设检验核的Hough变换算法","authors":"Palmer P.L., Petrou M., Kittler J.","doi":"10.1006/ciun.1993.1039","DOIUrl":null,"url":null,"abstract":"<div><p>In this paper we consider a Hough transform line finding algorithm in which the voting kernel is a smooth function of differences in <em>both</em> line parameters. The shape of the voting kernel is decided in terms of a hypothesis testing approach, and the shape is adjusted to give optimal results. We show that this new kernel is robust to changes in the distribution of the underlying noise and the implementation is very fast, taking typically 2-3 s on a Sparc 2 workstation for a 256 × 256 image.</p></div>","PeriodicalId":100350,"journal":{"name":"CVGIP: Image Understanding","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1993-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/ciun.1993.1039","citationCount":"0","resultStr":"{\"title\":\"A Hough Transform Algorithm with a 2D Hypothesis Testing Kernel\",\"authors\":\"Palmer P.L., Petrou M., Kittler J.\",\"doi\":\"10.1006/ciun.1993.1039\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In this paper we consider a Hough transform line finding algorithm in which the voting kernel is a smooth function of differences in <em>both</em> line parameters. The shape of the voting kernel is decided in terms of a hypothesis testing approach, and the shape is adjusted to give optimal results. We show that this new kernel is robust to changes in the distribution of the underlying noise and the implementation is very fast, taking typically 2-3 s on a Sparc 2 workstation for a 256 × 256 image.</p></div>\",\"PeriodicalId\":100350,\"journal\":{\"name\":\"CVGIP: Image Understanding\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1993-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1006/ciun.1993.1039\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"CVGIP: Image Understanding\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1049966083710399\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"CVGIP: Image Understanding","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1049966083710399","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Hough Transform Algorithm with a 2D Hypothesis Testing Kernel
In this paper we consider a Hough transform line finding algorithm in which the voting kernel is a smooth function of differences in both line parameters. The shape of the voting kernel is decided in terms of a hypothesis testing approach, and the shape is adjusted to give optimal results. We show that this new kernel is robust to changes in the distribution of the underlying noise and the implementation is very fast, taking typically 2-3 s on a Sparc 2 workstation for a 256 × 256 image.
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