Automatic detection of calibration grids in time-of-flight images. Hansard, M., Horaud, R., Amat, M., & Evangelidis, G. Computer Vision and Image Understanding. 00002
Automatic detection of calibration grids in time-of-flight images [link]Paper  doi  abstract   bibtex   
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.
@article{ hansard_automatic_????,
  title = {Automatic detection of calibration grids in time-of-flight images},
  issn = {1077-3142},
  url = {http://www.sciencedirect.com/science/article/pii/S1077314214000149},
  doi = {10.1016/j.cviu.2014.01.007},
  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.},
  urldate = {2014-02-20TZ},
  journal = {Computer Vision and Image Understanding},
  author = {Hansard, Miles and Horaud, Radu and Amat, Michel and Evangelidis, Georgios},
  note = {00002},
  keywords = {calibration}
}
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