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Learning Mixtures of Weighted TreeUnions by Minimizing
General Linear Cameras
121 other sections not shown
affine affine combinations algorithm analysis applied approach audio background boundary calibration camera model color Computer Vision Conf constraints contour coordinate corresponding covariance covariance matrix curve CVPR defined derivatives detection distance dynamic texture ECCV edge edge detection epipolar epipolar geometry epipoles equation error estimation evolution extended fragments extrinsic camera parameters face Figure filter frame function Gaussian geometry given gradient homography IEEE IEEE Trans illumination image points inpainting invariant iterative kernel linear Matas Eds matching matrix mean shift medial axis method minimal motion nodes noise obtained optic disc optical flow optimal orientation pairs Pajdla performance pixels plane POPs pose problem Proc projection projection matrices proposed radiance rays reconstruction region representation robust sample scale scene segmentation sequence shape shown shows smooth solution space spatial stereo structure surface Symmetry Set technique tensor texture texture synthesis Theorem tracking values vector views visual voxel