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We extend planar fiducial markers using random dots [8] to nonrigidly deformable markers. Because the recognition and tracking of random dot markers are based on keypoint matching, we can estimate the deformation of the markers with nonrigid surface detection from keypoint correspondences. First, the initial pose of the markers is computed from a homography with RANSAC as a planar detection. Second,...
This paper presents an approach for detecting and tracking various types of planar objects with geometrical features. We combine traditional keypoint detectors with Locally Likely Arrangement Hashing (LLAH) [21] for geometrical feature based keypoint matching. Because the stability of keypoint extraction affects the accuracy of the keypoint matching, we set the criteria of keypoint selection on keypoint...
We extend planar fiducial markers using random dots [8] to non-rigidly deformable markers. Because the recognition and tracking of random dot markers are based on keypoint matching, we can estimate the deformation of the markers with nonrigid surface detection from keypoint correspondences. First, the initial pose of the markers is computed from a homography with RANSAC as a planar detection. Second,...
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