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

Miles Hansard (1) , Radu Horaud (2) , Michel Amat (2) , Georgios Evangelidis (2)

1 EECS - School of Electronic Engineering and Computer Science
2 PERCEPTION - Interpretation and Modelling of Images and Videos

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.

Mots clés: Range imaging; Time-of-flight sensors; Camera calibration; Hough transform

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

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⟩