Statistical Optimization for Geometric Computation: Theory and Practice - 1st Edition - ISBN: 9780444824271, 9780080544823

Statistical Optimization for Geometric Computation: Theory and Practice, Volume 18

1st Edition

Authors: K. Kanatani
eBook ISBN: 9780080544823
Imprint: North Holland
Published Date: 14th March 1996


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Table of Contents

1. Introduction. 1.1. The aims of this book. 1.2. The Features of this book. 1.3. Organization and background. 1.4. The analytical mind: Strength and weakness. 2. Fundamentals of Linear Algebra. 2.1. Vector and matrix calculus. 2.2. Eigenvalue problem. 2.3. Linear systems and optimization. 2.4. Matrix and tensor algebra. 3. Probabilities and Statistical Estimation. 3.1. Probability distributions. 3.2. Manifolds and local distributions. 3.3. Gaussian distributions and &khgr;2 distributions. 3.4. Statistical estimation for gaussian models. 3.5. General statistical estimation. 3.6. Maximum likelihood estimation. 3.7. Akaike information criterion. 4. Representation of Geometric Objects. 4.1. Image points and image lines. 4.2. Space points and space lines. 4.3. Space planes. 4.4. Conics. 4.5. Space conics and quadrics. 4.6. Coordinate transformation and projection. 5. Geometric Correction. 5.1. General theory. 5.2. Correction of image points and image lines. 5.3. Correction of space points and space lines. 5.4. Correction of space planes. 5.5. Orthogonality correction. 5.6. Conic incidence correction. 6. 3-D Computation by Stereo Vision. 6.1. Epipolar constraint. 6.2. Optimal correction of correspondence. 6.3. 3-D reconstruction of points. 6.4. 3-D reconstruction of lines. 6.5. Optimal back projection onto a space plane. 6.6. Scenes infinitely far away. 6.7. Camera calibration errors. 7. Parametric Fitting. 7.1. General theory. 7.2 Optimal fitting for image points. 7.3. Optimal fitting for image lines. 7.4. Optimal fitting for space points. 7.5. Optimal fitting for space lines. 7.6. Optimal fitting for space planes. 8. Optimal Filter. 8.1. General theory. 8.2. Iterative estimation scheme. 8.3. Effective gradient approximation. 8.4. Reduction from the kalman filter. 8.5. Estimation from linear hypotheses. 9. Renormaliza


This book discusses mathematical foundations of statistical inference for building a 3-D model of the environment from image and sensor data that contain noise - a central task for autonomous robots guided by video cameras and sensors. A theoretical accuracy bound is derived for the optimization procedure for maximizing the reliability of the estimation based on noisy data, and practical computational schemes that attain that bound are derived. Many synthetic and real data examples are given to demonstrate that conventional methods are not optimal and how accuracy improves if truly optimal methods are employed.

Institutions to benefit from this book include, University departments related to computer science, information processing, image processing, robotics and mechatronics, governmental research organizations for computer-related advanced technology and corporate laboratories of computer and electronic industries.


© North Holland 1996
North Holland
eBook ISBN:


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About the Authors

K. Kanatani Author

Affiliations and Expertise

Gunma University, Japan