{"title":"作为逆光学的计算视觉:重建光谱反射率和光源","authors":"T. Poggio, A. Hurlbert","doi":"10.1364/srs.1986.fd2","DOIUrl":null,"url":null,"abstract":"The standard definition of computational vision is that it is inverse optics. The direct problem - the problem of classical optics - or computer graphics - is to determine the images of three-dimensional objects. Computational vision is confronted with the inverse and ill-posed problems of recovering surface properties from the partial information contained in images. As a consequence, vision must rely on natural constraints, that is, general assumptions about the physical world to derive an ambiguous output.","PeriodicalId":262149,"journal":{"name":"Topical Meeting On Signal Recovery and Synthesis II","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Computational vision as inverse optics: reconstructing spectral reflectances and illuminant\",\"authors\":\"T. Poggio, A. Hurlbert\",\"doi\":\"10.1364/srs.1986.fd2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The standard definition of computational vision is that it is inverse optics. The direct problem - the problem of classical optics - or computer graphics - is to determine the images of three-dimensional objects. Computational vision is confronted with the inverse and ill-posed problems of recovering surface properties from the partial information contained in images. As a consequence, vision must rely on natural constraints, that is, general assumptions about the physical world to derive an ambiguous output.\",\"PeriodicalId\":262149,\"journal\":{\"name\":\"Topical Meeting On Signal Recovery and Synthesis II\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Topical Meeting On Signal Recovery and Synthesis II\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1364/srs.1986.fd2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Topical Meeting On Signal Recovery and Synthesis II","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1364/srs.1986.fd2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Computational vision as inverse optics: reconstructing spectral reflectances and illuminant
The standard definition of computational vision is that it is inverse optics. The direct problem - the problem of classical optics - or computer graphics - is to determine the images of three-dimensional objects. Computational vision is confronted with the inverse and ill-posed problems of recovering surface properties from the partial information contained in images. As a consequence, vision must rely on natural constraints, that is, general assumptions about the physical world to derive an ambiguous output.