{"title":"求解相机自标定Kruppa方程的新方法","authors":"Lei Cheng, Fuchao Wu, Zhanyi Hu, H. Tsui","doi":"10.1109/ICPR.2002.1048301","DOIUrl":null,"url":null,"abstract":"We propose an approach to solving the Kruppa equations for camera self-calibration. Traditionally, the unknown scale factors in the Kruppa equations are eliminated first, leading to a set of nonlinear constraints. Instead, we determine the scale factors by a Levenberg-Marquardt optimization or genetic optimization technique first. Then, the camera's intrinsic parameters are derived from the resulting linear constraints. Extensive simulations as well as experiments with real images verify that the above technique is both accurate and robust.","PeriodicalId":159502,"journal":{"name":"Object recognition supported by user interaction for service robots","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"A new approach to solving Kruppa equations for camera self-calibration\",\"authors\":\"Lei Cheng, Fuchao Wu, Zhanyi Hu, H. Tsui\",\"doi\":\"10.1109/ICPR.2002.1048301\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose an approach to solving the Kruppa equations for camera self-calibration. Traditionally, the unknown scale factors in the Kruppa equations are eliminated first, leading to a set of nonlinear constraints. Instead, we determine the scale factors by a Levenberg-Marquardt optimization or genetic optimization technique first. Then, the camera's intrinsic parameters are derived from the resulting linear constraints. Extensive simulations as well as experiments with real images verify that the above technique is both accurate and robust.\",\"PeriodicalId\":159502,\"journal\":{\"name\":\"Object recognition supported by user interaction for service robots\",\"volume\":\"38 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-12-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Object recognition supported by user interaction for service robots\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPR.2002.1048301\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Object recognition supported by user interaction for service robots","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPR.2002.1048301","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A new approach to solving Kruppa equations for camera self-calibration
We propose an approach to solving the Kruppa equations for camera self-calibration. Traditionally, the unknown scale factors in the Kruppa equations are eliminated first, leading to a set of nonlinear constraints. Instead, we determine the scale factors by a Levenberg-Marquardt optimization or genetic optimization technique first. Then, the camera's intrinsic parameters are derived from the resulting linear constraints. Extensive simulations as well as experiments with real images verify that the above technique is both accurate and robust.