Proceedings: CVPR, Volumes 1-2IEEE Computer Society Press, 1983 - Computer vision |
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Page 51
... camera position must be represented in the projective grid space . Although we have only fundamental matrices relating every image to basic view images , the position of every image's camera can be derived from the fundamental matrices ...
... camera position must be represented in the projective grid space . Although we have only fundamental matrices relating every image to basic view images , the position of every image's camera can be derived from the fundamental matrices ...
Page 150
... camera coordinate frame . - We denote the successive camera positions corre- sponding to the input images by P ; = KRŢ ( I | — T ; ) where Ro I and To = 0 , i.e. , the first camera po- sition is at the origin of the model frame ...
... camera coordinate frame . - We denote the successive camera positions corre- sponding to the input images by P ; = KRŢ ( I | — T ; ) where Ro I and To = 0 , i.e. , the first camera po- sition is at the origin of the model frame ...
Page 353
... cameras , the Euclidean structure of 3 - D world points using multiple views from known positions . We are free to alter the internal parameters of the camera during these operations . Our initial experiments demonstrate the ...
... cameras , the Euclidean structure of 3 - D world points using multiple views from known positions . We are free to alter the internal parameters of the camera during these operations . Our initial experiments demonstrate the ...
Contents
A Projective Framework for Scene Segmentation in the Presence | 2 |
Learning 2D Shape Models | 8 |
Affine | 9 |
Copyright | |
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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 normal 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 similarity solution space spatial stereo structure superquadrics surface techniques texture tion tracking trajectory transformation values vector visual ZIP Code