{"title":"使用局部频率表示计算离焦模糊的深度","authors":"Mats Gokstorp","doi":"10.1109/ICPR.1994.576248","DOIUrl":null,"url":null,"abstract":"We present a method to compute depth from the amount of defocus in two images obtained from the same view-point but with different camera parameter settings. The change in defocus (blur) between the two images is proportional to the depth in the scene. We introduce a novel method to estimate the blur using a multiresolution local frequency representation of the input image pair. A confidence measure is used to discriminate between high error and low error blur estimates. Quantitative experimental results are shown for both real and synthetic images.","PeriodicalId":312019,"journal":{"name":"Proceedings of 12th International Conference on Pattern Recognition","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"Computing depth from out-of-focus blur using a local frequency representation\",\"authors\":\"Mats Gokstorp\",\"doi\":\"10.1109/ICPR.1994.576248\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a method to compute depth from the amount of defocus in two images obtained from the same view-point but with different camera parameter settings. The change in defocus (blur) between the two images is proportional to the depth in the scene. We introduce a novel method to estimate the blur using a multiresolution local frequency representation of the input image pair. A confidence measure is used to discriminate between high error and low error blur estimates. Quantitative experimental results are shown for both real and synthetic images.\",\"PeriodicalId\":312019,\"journal\":{\"name\":\"Proceedings of 12th International Conference on Pattern Recognition\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-10-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 12th International Conference on Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPR.1994.576248\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 12th International Conference on Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPR.1994.576248","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Computing depth from out-of-focus blur using a local frequency representation
We present a method to compute depth from the amount of defocus in two images obtained from the same view-point but with different camera parameter settings. The change in defocus (blur) between the two images is proportional to the depth in the scene. We introduce a novel method to estimate the blur using a multiresolution local frequency representation of the input image pair. A confidence measure is used to discriminate between high error and low error blur estimates. Quantitative experimental results are shown for both real and synthetic images.