Article ID Journal Published Year Pages File Type
525685 Computer Vision and Image Understanding 2014 11 Pages PDF
Abstract

•Standard corner-detection methods are susceptible to sampling artifacts.•A Hough-based method fits a global model to the chequerboard line pattern.•This process less sensitive to low-resolution: each vertex is defined by the intersection of 2 lines.•Double-mapping segments the gradient vectors; detection is split into 2 Hough transforms.•The method is evaluated over 700 detections and performs better than OpenCV method.

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.

Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
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