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3-D object algorithm applications approach Attached to End axis Besl and Jain binary image boundary camera characteristics component computer vision consider constraints critical points curve defined depth map detection determined digital image Digital Image Processing direction discussed dynamic scene edge detector End of Paper Figures Attached filter frame Gaussian curvature geometric graph gray level histogram Hough transform IEEE image processing input inspection intensity-image Jain Revised Version labels matching matrix mean curvature method motion moving objects noise object models object recognition obtained optical flow orientation output parameters partial derivatives pixel planar principal curvatures Proc Proe projection properties quantization range data range image range-image rangefinder ray sums reconstruction regions represent representation Robot Robot Vision rotation second fundamental form segmentation sensor data shown in Figure signal solder joint spatial structure subimages techniques Three-Dimensional threshold tion transform vector vision systems visual x-ray