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Journal Articles Computer Vision and Image Understanding Year : 2014

Automatic Detection of Calibration Grids in Time-of-Flight Images

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Abstract

It is convenient to calibrate time-of-flight cameras by established methods, using images of a chequerboard pattern. The low resolution of the amplitude image, however, makes it difficult to detect the board reliably. Heuristic detection methods, based on connected image-components, perform very poorly on this data. An alternative, geometrically-principled method is introduced here, based on the Hough transform. The projection of a chequerboard is represented by two pencils of lines, which are identified as oriented clusters in the gradient-data of the image. A projective Hough transform is applied to each of the two clusters, in axis-aligned coordinates. The range of each transform is properly bounded, because the corresponding gradient vectors are approximately parallel. Each of the two transforms contains a series of collinear peaks; one for every line in the given pencil. This pattern is easily detected, by sweeping a dual line through the transform. The proposed Hough-based method is compared to the standard OpenCV detection routine, by application to several hundred time-of-flight images. It is shown that the new method detects significantly more calibration boards, over a greater variety of poses, without any overall loss of accuracy. This conclusion is based on an analysis of both geometric and photometric error.
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Dates and versions

hal-00936333 , version 1 (24-01-2014)
hal-00936333 , version 2 (25-01-2014)

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Miles Hansard, Radu Horaud, Michel Amat, Georgios Evangelidis. Automatic Detection of Calibration Grids in Time-of-Flight Images. Computer Vision and Image Understanding, 2014, 121, pp.108-118. ⟨10.1016/j.cviu.2014.01.007⟩. ⟨hal-00936333v2⟩
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