{"id":597,"date":"2017-06-30T11:46:03","date_gmt":"2017-06-30T10:46:03","guid":{"rendered":"http:\/\/babel.isa.uma.es\/kipr\/?p=597"},"modified":"2017-06-30T11:46:03","modified_gmt":"2017-06-30T10:46:03","slug":"a-nice-general-model-for-camera-calibration","status":"publish","type":"post","link":"https:\/\/babel.isa.uma.es\/kipr\/?p=597","title":{"rendered":"A nice general model for camera calibration"},"content":{"rendered":"<h4>S. Ramalingam and P. Sturm, <strong>&#8220;A Unifying Model for Camera Calibration,&#8221;<\/strong> in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 7, pp. 1309-1319, July 1 2017. <a href=\"http:\/\/doi.org\/10.1109\/TPAMI.2016.2592904\" target=\"_blank\">DOI: 10.1109\/TPAMI.2016.2592904<\/a>.<\/h4>\n<blockquote><p>This paper proposes a unified theory for calibrating a wide variety of camera models such as pinhole, fisheye, cata-dioptric, and multi-camera networks. We model any camera as a set of image pixels and their associated camera rays in space. Every pixel measures the light traveling along a (half-) ray in 3-space, associated with that pixel. By this definition, calibration simply refers to the computation of the mapping between pixels and the associated 3D rays. Such a mapping can be computed using images of calibration grids, which are objects with known 3D geometry, taken from unknown positions. This general camera model allows to represent non-central cameras; we also consider two special subclasses, namely central and axial cameras. In a central camera, all rays intersect in a single point, whereas the rays are completely arbitrary in a non-central one. Axial cameras are an intermediate case: the camera rays intersect a single line. In this work, we show the theory for calibrating central, axial and non-central models using calibration grids, which can be either three-dimensional or planar.<\/p><\/blockquote>\n","protected":false},"excerpt":{"rendered":"<p>S. Ramalingam and P. Sturm, &#8220;A Unifying Model for Camera Calibration,&#8221; in IEEE Transactions on Pattern Analysis and Machine Intelligence, <span class=\"ellipsis\">&hellip;<\/span> <span class=\"more-link-wrap\"><a href=\"https:\/\/babel.isa.uma.es\/kipr\/?p=597\" class=\"more-link\"><span>Read More &rarr;<\/span><\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[64],"tags":[246],"class_list":["post-597","post","type-post","status-publish","format-standard","hentry","category-computer-vision","tag-camera-calibration"],"_links":{"self":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/597"}],"collection":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=597"}],"version-history":[{"count":1,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/597\/revisions"}],"predecessor-version":[{"id":598,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/597\/revisions\/598"}],"wp:attachment":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=597"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=597"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=597"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}