{"title":"相机模型的主要误差是相机模型中系统视角偏差的倾斜轴假设修正","authors":"Guy Martin","doi":"10.1109/TePRA.2015.7219697","DOIUrl":null,"url":null,"abstract":"The tilted assumption, a remnant of analog cameras, is still in use. It seeks to compensate the off squareness of the image plane with the lens axis, but in fact creates a systematic shape alteration by introducing a scale variation in the image, and adds shear with the use of a skew parameter. It results in an image center bias which in turn offsets every single parameter estimate in the camera model. We disclose our own exact solution to the internal camera model, modeling the image plane as a pure projection, our related camera calibration method, and discuss the various improvements resulting from our find in almost every performance aspect of digital imaging.","PeriodicalId":325788,"journal":{"name":"2015 IEEE International Conference on Technologies for Practical Robot Applications (TePRA)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The major error in the camera model is the tilted axis assumption correction of a systematic perspective bias in the camera model\",\"authors\":\"Guy Martin\",\"doi\":\"10.1109/TePRA.2015.7219697\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The tilted assumption, a remnant of analog cameras, is still in use. It seeks to compensate the off squareness of the image plane with the lens axis, but in fact creates a systematic shape alteration by introducing a scale variation in the image, and adds shear with the use of a skew parameter. It results in an image center bias which in turn offsets every single parameter estimate in the camera model. We disclose our own exact solution to the internal camera model, modeling the image plane as a pure projection, our related camera calibration method, and discuss the various improvements resulting from our find in almost every performance aspect of digital imaging.\",\"PeriodicalId\":325788,\"journal\":{\"name\":\"2015 IEEE International Conference on Technologies for Practical Robot Applications (TePRA)\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-05-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Conference on Technologies for Practical Robot Applications (TePRA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TePRA.2015.7219697\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Conference on Technologies for Practical Robot Applications (TePRA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TePRA.2015.7219697","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The major error in the camera model is the tilted axis assumption correction of a systematic perspective bias in the camera model
The tilted assumption, a remnant of analog cameras, is still in use. It seeks to compensate the off squareness of the image plane with the lens axis, but in fact creates a systematic shape alteration by introducing a scale variation in the image, and adds shear with the use of a skew parameter. It results in an image center bias which in turn offsets every single parameter estimate in the camera model. We disclose our own exact solution to the internal camera model, modeling the image plane as a pure projection, our related camera calibration method, and discuss the various improvements resulting from our find in almost every performance aspect of digital imaging.