{"title":"受限摄像机估计的一种简单标定方法","authors":"Bin Kong, Fei Zheng, Tingjian Fang","doi":"10.1109/ICIA.2005.1635144","DOIUrl":null,"url":null,"abstract":"In real applications, there is often the need of estimating the intrinsic and extrinsic parameters of a camera directly from the images of natural scenes or working scenarios. When there are only several feature points can be determined, the results of current calibration methods are very unstable. In this paper, a geometrical method is presented for estimating a restricted camera. It can estimate f even when only one feature point is valid. Experiments show that it has good robustness and reliability, and it is tolerant to multiple error sources.","PeriodicalId":136611,"journal":{"name":"2005 IEEE International Conference on Information Acquisition","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A simple calibration method for restricted camera estimation\",\"authors\":\"Bin Kong, Fei Zheng, Tingjian Fang\",\"doi\":\"10.1109/ICIA.2005.1635144\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In real applications, there is often the need of estimating the intrinsic and extrinsic parameters of a camera directly from the images of natural scenes or working scenarios. When there are only several feature points can be determined, the results of current calibration methods are very unstable. In this paper, a geometrical method is presented for estimating a restricted camera. It can estimate f even when only one feature point is valid. Experiments show that it has good robustness and reliability, and it is tolerant to multiple error sources.\",\"PeriodicalId\":136611,\"journal\":{\"name\":\"2005 IEEE International Conference on Information Acquisition\",\"volume\":\"3 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\":\"2005 IEEE International Conference on Information Acquisition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIA.2005.1635144\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2005 IEEE International Conference on Information Acquisition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIA.2005.1635144","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A simple calibration method for restricted camera estimation
In real applications, there is often the need of estimating the intrinsic and extrinsic parameters of a camera directly from the images of natural scenes or working scenarios. When there are only several feature points can be determined, the results of current calibration methods are very unstable. In this paper, a geometrical method is presented for estimating a restricted camera. It can estimate f even when only one feature point is valid. Experiments show that it has good robustness and reliability, and it is tolerant to multiple error sources.