Computer Vision and Image ProcessingUniversity of Michigan, College of Engineering, 1986 - Computer vision |
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3-D object algorithm analysis applications approach approximation array axis Besl binary image boundary camera characteristics component computer vision considered coordinate critical points curve Cytocomputer defined depth map derivative detector digital image digital image processing discussed edge detection filter frame function Gaussian curvature geometric graph gray levels histogram Hough transform hypothesis IEEE image processing input intensity images labels machine machine vision matching matrix mean curvature memory method neighborhood noise object models object recognition obtained operations optical flow orientation output parallel parameters Pattern Recognition performed picture pipeline pixel planar plane possible object principal curvatures problem processors projection range data range images reconstruction represent representation robot rotation scene segmentation sensor data shape shown in Figure solder joint spatial structure subimage surface fitting surface region techniques template three-dimensional threshold tion transformations types vector vision systems window x-ray zero