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16 August 2017 Active learning in camera calibration through vision measurement application
Xiaoqin Li, Jierong Guo, Xianchun Wang, Changqing Liu, Binfang Cao
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Proceedings Volume 10452, 14th Conference on Education and Training in Optics and Photonics: ETOP 2017; 104524O (2017) https://doi.org/10.1117/12.2269011
Event: 14th Conference on Education and Training in Optics and Photonics, ETOP 2017, 2017, Hangzhou, China
Abstract
Since cameras are increasingly more used in scientific application as well as in the applications requiring precise visual information, effective calibration of such cameras is getting more important. There are many reasons why the measurements of objects are not accurate. The largest reason is that the lens has a distortion. Another detrimental influence on the evaluation accuracy is caused by the perspective distortions in the image. They happen whenever we cannot mount the camera perpendicularly to the objects we want to measure. In overall, it is very important for students to understand how to correct lens distortions, that is camera calibration. If the camera is calibrated, the images are rectificated, and then it is possible to obtain undistorted measurements in world coordinates. This paper presents how the students should develop a sense of active learning for mathematical camera model besides the theoretical scientific basics. The authors will present the theoretical and practical lectures which have the goal of deepening the students understanding of the mathematical models of area scan cameras and building some practical vision measurement process by themselves.
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Xiaoqin Li, Jierong Guo, Xianchun Wang, Changqing Liu, and Binfang Cao "Active learning in camera calibration through vision measurement application", Proc. SPIE 10452, 14th Conference on Education and Training in Optics and Photonics: ETOP 2017, 104524O (16 August 2017); https://doi.org/10.1117/12.2269011
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KEYWORDS
Cameras
Calibration
Distortion
Machine vision
Mathematical modeling
Imaging systems
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