{"title":"视觉的物理不变量","authors":"I. Weiss","doi":"10.1109/PBMCV.1995.514668","DOIUrl":null,"url":null,"abstract":"Unlike geometric invariants, the invariants described here concern\nthe physical processes that form images, involving shading, IR, radar,\nsonar, etc. The image formed by such a process depends on many variables\nin addition to the geometry, such as the characteristics of the lighting\nor other incident radiation, the imaging system, etc. Most of these\nvariables are not known in advance, so the recovery of shape is\ndifficult. The problem could be greatly simplified if we could find\ninvariants of the situation, namely quantities that stay unchanged as\nsome of the unknown variables change. In this paper we apply known\nmethods of mathematical physics to finding invariants of physical\nimaging processes. These methods take advantage of various symmetries,\nwhich can be part of a model-based approach to recognition. As an\nexample we use the shape from shading problem, but the methods have a\nmuch wider applicability","PeriodicalId":343932,"journal":{"name":"Proceedings of the Workshop on Physics-Based Modeling in Computer Vision","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Physics-like invariants for vision\",\"authors\":\"I. Weiss\",\"doi\":\"10.1109/PBMCV.1995.514668\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Unlike geometric invariants, the invariants described here concern\\nthe physical processes that form images, involving shading, IR, radar,\\nsonar, etc. The image formed by such a process depends on many variables\\nin addition to the geometry, such as the characteristics of the lighting\\nor other incident radiation, the imaging system, etc. Most of these\\nvariables are not known in advance, so the recovery of shape is\\ndifficult. The problem could be greatly simplified if we could find\\ninvariants of the situation, namely quantities that stay unchanged as\\nsome of the unknown variables change. In this paper we apply known\\nmethods of mathematical physics to finding invariants of physical\\nimaging processes. These methods take advantage of various symmetries,\\nwhich can be part of a model-based approach to recognition. As an\\nexample we use the shape from shading problem, but the methods have a\\nmuch wider applicability\",\"PeriodicalId\":343932,\"journal\":{\"name\":\"Proceedings of the Workshop on Physics-Based Modeling in Computer Vision\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1995-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Workshop on Physics-Based Modeling in Computer Vision\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PBMCV.1995.514668\",\"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 the Workshop on Physics-Based Modeling in Computer Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PBMCV.1995.514668","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Unlike geometric invariants, the invariants described here concern
the physical processes that form images, involving shading, IR, radar,
sonar, etc. The image formed by such a process depends on many variables
in addition to the geometry, such as the characteristics of the lighting
or other incident radiation, the imaging system, etc. Most of these
variables are not known in advance, so the recovery of shape is
difficult. The problem could be greatly simplified if we could find
invariants of the situation, namely quantities that stay unchanged as
some of the unknown variables change. In this paper we apply known
methods of mathematical physics to finding invariants of physical
imaging processes. These methods take advantage of various symmetries,
which can be part of a model-based approach to recognition. As an
example we use the shape from shading problem, but the methods have a
much wider applicability