{"title":"形态学中一类有效的交替顺序滤波器","authors":"Soo-Chang Pei , Chin-Lun Lai , Frank Y. Shih","doi":"10.1006/gmip.1996.0416","DOIUrl":null,"url":null,"abstract":"<div><p>In this note, an efficient class of alternating sequential filters (ASFs) in mathematical morphology is presented to reduce the computational complexity in the conventional ASFs about a half. The performance boundary curves of the new filters are provided. Experimental results from applying these new ASFs to texture classification and image filtering (grayscale and binary) show that comparable performance can be achieved while much of the computational complexity is reduced.</p></div>","PeriodicalId":100591,"journal":{"name":"Graphical Models and Image Processing","volume":"59 2","pages":"Pages 109-116"},"PeriodicalIF":0.0000,"publicationDate":"1997-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/gmip.1996.0416","citationCount":"24","resultStr":"{\"title\":\"An Efficient Class of Alternating Sequential Filters in Morphology\",\"authors\":\"Soo-Chang Pei , Chin-Lun Lai , Frank Y. Shih\",\"doi\":\"10.1006/gmip.1996.0416\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In this note, an efficient class of alternating sequential filters (ASFs) in mathematical morphology is presented to reduce the computational complexity in the conventional ASFs about a half. The performance boundary curves of the new filters are provided. Experimental results from applying these new ASFs to texture classification and image filtering (grayscale and binary) show that comparable performance can be achieved while much of the computational complexity is reduced.</p></div>\",\"PeriodicalId\":100591,\"journal\":{\"name\":\"Graphical Models and Image Processing\",\"volume\":\"59 2\",\"pages\":\"Pages 109-116\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1006/gmip.1996.0416\",\"citationCount\":\"24\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Graphical Models and Image Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1077316996904165\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Graphical Models and Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1077316996904165","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Efficient Class of Alternating Sequential Filters in Morphology
In this note, an efficient class of alternating sequential filters (ASFs) in mathematical morphology is presented to reduce the computational complexity in the conventional ASFs about a half. The performance boundary curves of the new filters are provided. Experimental results from applying these new ASFs to texture classification and image filtering (grayscale and binary) show that comparable performance can be achieved while much of the computational complexity is reduced.
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