IEEE Computer Vision and Pattern Recognition, Volumes 1-2
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A Projective Framework for Scene Segmentation in the Presence
Learning 2D Shape Models
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3D motion active contour affine affine transformation algorithm alignment analysis applied approach approximation background balloon model Bayesian Bayesian networks boundary camera classification cluster color Computer Vision constraint corresponding curve database defined deformable depth detection detector distance distribution dynamic contour edge energy equation error estimation example fingerprint frame framework function Gabor filters Gaussian geometric global gradient graph hypothesis IEEE image sequences input iterative Kalman filter linear matching matrix measure medial axis method minimization motion estimation moving objects multiple node noise obtained occlusion optical flow optimal PAMI parameters patches Pattern Recognition performance pixel planar plane points polynomial problem Proc projective reconstruction region representation robust rotation rotoscoping samples scene segmentation sensor shape shown in Figure shows signature similar solution space spatial stereo structure superquadrics surface techniques texture tion tracking trajectory transformation values vector visual voxel ZIP Code