Title:	Fisheye lens correction by estimating 3D location
Authors:	Ji-Won, Lee Byung-Uk, Lee
Citation:	Journal of WSCG. 2016, vol. 24, no. 1, p. 19-24.
Issue Date:	2016
Publisher:	Václav Skala - UNION Agency
Document type:	článek article
URI:	http://wscg.zcu.cz/WSCG2016/!_2016_Journal_WSCG-No-1.pdf http://hdl.handle.net/11025/21644
ISSN:	1213-6972 (print) 1213-6980 (CD-ROM) 1213-6964 (on-line)
Keywords:	objektiv rybí oko;zkreslení;korekce
Keywords in different language:	fisheye lens;distortion;correction
Abstract in different language:	Since a fisheye lens can capture a wide angle scenery, it is broadly used for surveillance or outdoor sports. However, acquired images suffer from severe geometric distortions. Most of the existing distortion correction algorithms depend on linear features: images of linear features are first identified and then 2 dimensional warping is applied to make the curved images look straight. We propose a novel fisheye distortion correction method that estimates 3 dimensional (3D) locations of a foreground first, and then projects them to an image plane by perspective projection. When we know approximate distance of the foreground object, as in cases of head mounted camera, we can assume the 3D object plane of the foreground, and then estimate the 3D location from image points after internal camera calibration. For head mounted camera, foreground is a face and body of a human, and distortion of human figure is quite unnatural and awkward. Moreover, human figures lack linear features which excludes the use of conventional 2D warping techniques. We present techniques to estimate the 3D position from a corresponding 2D image point, which enables calculation of 3D object location. And then apply perspective projection to the 3D object position to obtain a distortion-free image. We demonstrate the efficacy of the proposed method using fisheye camera images and the applicability of the proposed concept to real applications.
Rights:	© Václav Skala - UNION Agency
Appears in Collections:	Volume 24, Number 1 (2016)

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