{"title":"基于边缘基元的压缩图像表示","authors":"Altergartenberg R., Huck F.O., Narayanswamy R.","doi":"10.1006/cgip.1994.1001","DOIUrl":null,"url":null,"abstract":"<div><p>Bandpassed images, commonly used for edge detection, also retain information about intensities between the edge boundaries. Using the familiar Laplacian-of-Gaussian as a bandpass filter, we present a method to extract and code the edge-associated information (edge primitives) and recover an image representation with high structural fidelity. We demonstrate that the edge-primitives representation is compact and therefore can be coded with high compression ratios.</p></div>","PeriodicalId":100349,"journal":{"name":"CVGIP: Graphical Models and Image Processing","volume":"56 1","pages":"Pages 1-7"},"PeriodicalIF":0.0000,"publicationDate":"1994-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cgip.1994.1001","citationCount":"8","resultStr":"{\"title\":\"Compact Image Representation by Edge Primitives\",\"authors\":\"Altergartenberg R., Huck F.O., Narayanswamy R.\",\"doi\":\"10.1006/cgip.1994.1001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Bandpassed images, commonly used for edge detection, also retain information about intensities between the edge boundaries. Using the familiar Laplacian-of-Gaussian as a bandpass filter, we present a method to extract and code the edge-associated information (edge primitives) and recover an image representation with high structural fidelity. We demonstrate that the edge-primitives representation is compact and therefore can be coded with high compression ratios.</p></div>\",\"PeriodicalId\":100349,\"journal\":{\"name\":\"CVGIP: Graphical Models and Image Processing\",\"volume\":\"56 1\",\"pages\":\"Pages 1-7\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1006/cgip.1994.1001\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"CVGIP: Graphical Models and Image Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1049965284710017\",\"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: Graphical Models and Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1049965284710017","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Bandpassed images, commonly used for edge detection, also retain information about intensities between the edge boundaries. Using the familiar Laplacian-of-Gaussian as a bandpass filter, we present a method to extract and code the edge-associated information (edge primitives) and recover an image representation with high structural fidelity. We demonstrate that the edge-primitives representation is compact and therefore can be coded with high compression ratios.
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