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Page 76

§5.6 Tables of Estimated Time and Space Complexity in the Situations

in 5.1-5.5. Let N - number of points in the dense grid which would be used in

either method

to ...

§5.6 Tables of Estimated Time and Space Complexity in the Situations

**Described**in 5.1-5.5. Let N - number of points in the dense grid which would be used in

either method

**described**in 3.1 or 3.29. Let r = number of points at which we wishto ...

Page 163

era [o Fig.2.1 Four Direction Loci Feature (FD Loci Feature)

four numbers, x,y,z and w. There are two hundred and fifty six sets, because of x,y

,z and w is zero, one, two or three independently (4x4x4x4=256; . A pattern is ...

era [o Fig.2.1 Four Direction Loci Feature (FD Loci Feature)

**described**as a set offour numbers, x,y,z and w. There are two hundred and fifty six sets, because of x,y

,z and w is zero, one, two or three independently (4x4x4x4=256; . A pattern is ...

Page 568

The ganglion cell recordings and the H-G fits

of both optical and neural factors. The neural factors can be separated out by

deconvolution of the ganglion cell PWF with the expected optical diffraction

pattern.

The ganglion cell recordings and the H-G fits

**described**here include the effectsof both optical and neural factors. The neural factors can be separated out by

deconvolution of the ganglion cell PWF with the expected optical diffraction

pattern.

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### Contents

Depth from Three Camera Stereo | 2 |

Range and Shape Measurement Using ThreeView Stereo Analysis | 9 |

The Calibration Problem for Stereo | 15 |

Copyright | |

58 other sections not shown

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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 corner detection 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 method motion node noise object obtained octree operations optical flow orientation output parallel parameters Pattern Recognition performance perspective projection pixel planar polygonal problem Proc procedure processors projection quadtree region representation rotation scene sequence shape SIMD smoothing solution space step stereo structure subimage surface surface normal technique template tensor texture Theorem threshold tion transformation tree values vector visual window zero-crossings