Method for fully automatically detecting chessboard corner points

    公开(公告)号:US12094152B2

    公开(公告)日:2024-09-17

    申请号:US17442937

    申请日:2020-03-05

    CPC classification number: G06T7/70 G06T7/90

    Abstract: The present invention discloses a method for fully automatically detecting chessboard corner points, and belongs to the field of image processing and computer vision. Full automatic detection of chessboard corner points is completed by setting one or a plurality of marks with colors or certain shapes on a chessboard to mark an initial position, shooting an image and conducting corresponding processing, using a homography matrix H calculated by initial pixel coordinates of a unit grid in a pixel coordinate system and manually set world coordinates in a world coordinate system to expand outwards, and finally spreading to the whole chessboard region. The method has the advantages of simple procedure and easy implementation; the principle of expanding outwards by a homography matrix is used, so that the running speed of the algorithm is fast; and the corner points obtained by a robustness enhancement algorithm is more accurate, so that the situation of inaccurate corner point detection in the condition of complex illumination is avoided.

    Ellipse detection acceleration method based on generalized Pascal mapping

    公开(公告)号:US11783502B2

    公开(公告)日:2023-10-10

    申请号:US17437234

    申请日:2021-03-05

    CPC classification number: G06T7/60 G06T7/13

    Abstract: The present invention relates to the technical field of digital image processing, and provides an ellipse detection acceleration method based on generalized Pascal mapping. The method comprises: step 100, extracting accurate edge points from a real image by means an edge detection method of an ellipse detection method, connecting edge points into arcs, and taking a de-noised arc set as input of an ellipse detection acceleration method; step 200, screening out a valid candidate arc combinations probably belonging to the same ellipse from the arc set input in step 100; step 300, calculating five parameters of a candidate ellipse; repeating step 200 to step 300 until all valid candidate arc combinations in the arc set and corresponding candidate ellipses are found; and step 400, clustering and verifying candidate ellipse sets, obtaining a final detected ellipse set.

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