Self calibration of camera with non-linear imaging model

Wenguang Hou; Tao Shang

doi:10.1117/12.748805

15 November 2007 Self calibration of camera with non-linear imaging model

Wenguang Hou, Tao Shang

Proceedings Volume 6788, MIPPR 2007: Pattern Recognition and Computer Vision; 67880Z (2007) https://doi.org/10.1117/12.748805
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

Being put forward by the researchers in computer vision, self calibration commonly deals with camera with linear model. Since the distortion is practically existed especially for ordinary camera, the result of calibration can't meet the demand of vision measurement with high accuracy regardless of the distortion. Being obedience to systematism mainly, the distortion is the target function of distortion coefficient, principal point, principal distance ratio and skew factor etc. So there exists a group of parameters including of distortion coefficient, principal point, principal distance ratio and skew factor and fundamental matrix which make homologous point meets epipolar restriction theoretically. Accordingly, the paper advances the way titled self calibration of camera with non-linear imaging model which is on basis of the Kruppa equation. In calculating the fundamental matrix, we can obtain interior elements except principal distance by taking into account distortion correction about image coordinate. Then the principal distance can be obtained by using Kruppa equation. This way only need some homologous points between two images, not need any known information about objects. Lots of experiments have proven its correctness and reliability.

Citation Download Citation

Wenguang Hou and Tao Shang "Self calibration of camera with non-linear imaging model", Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67880Z (15 November 2007); https://doi.org/10.1117/12.748805

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