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Page 176
... estimate the unknown parameters , given NЄ [ 2,20 ] and M € [ 2 , 10 ] . Image plane noise levels are varied from .01 % to 17 % of the object image size . Cramér - Rao lower bounds ( CRLB ) are computed on the estimate mean square error ...
... estimate the unknown parameters , given NЄ [ 2,20 ] and M € [ 2 , 10 ] . Image plane noise levels are varied from .01 % to 17 % of the object image size . Cramér - Rao lower bounds ( CRLB ) are computed on the estimate mean square error ...
Page 215
... estimate of X * . Since the computation of CVMSE ( X ) does not require knowledge about σ , this leads to a practical method for estimation of X * . III.2 . Finding the Cross - Validation Estimate we In order to compute the cross ...
... estimate of X * . Since the computation of CVMSE ( X ) does not require knowledge about σ , this leads to a practical method for estimation of X * . III.2 . Finding the Cross - Validation Estimate we In order to compute the cross ...
Page 248
the parameters we estimate are correct . This property of over- constraint comes from using models : when we have used some points on a surface to estimate 3 - D parameters , we can check if we are correct by examining additional points ...
the parameters we estimate are correct . This property of over- constraint comes from using models : when we have used some points on a surface to estimate 3 - D parameters , we can check if we are correct by examining additional points ...
Contents
Depth from Three Camera Stereo | 2 |
The Calibration Problem for Stereo | 15 |
Model Based Analysis of Industrial Scenes | 28 |
Copyright | |
41 other sections not shown
Common terms and phrases
affine transform algorithm analysis angle applied approach approximation array Artificial Intelligence axis binary boundary calibration camera clustering component Computer Vision connected constraints contour convolution coordinate system corresponding curvature curve defined derivative described detector determined direction edge detection elements equation error estimate filter function Gaussian geometric given gradient histogram Hough transform IEEE IEEE Trans image plane Image Processing implementation input label line segments linear machine machine vision matching matrix measure merging method motion node noise object obtained octree operations optical flow orientation output parallel parameters Pattern Recognition perspective projection pixel planar polygon problem Proc procedure processors projection quadtree region representation rotation scene shape SIMD smoothing solution space step stereo structure surface surface normal technique template tensor texture Theorem threshold tion transformation tree values vector visual window zero-crossings