Statistical optimization for geometric computation : theory and practice
著者
書誌事項
Statistical optimization for geometric computation : theory and practice
（Machine intelligence and pattern recognition, 18）
Elsevier, c1996
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
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 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. Renormalization. 9.1. Eigenvector fit. 9.2. Unbiased eigenvector fit. 9.3. Generalized eigenvalue fit. 9.4 Renormalization. 9.5. Lincarization. 9.6. Second order renormalization. 10. Applications of Geometric Estimation. 10.1. Image line fitting. 10.2. Conic fitting. 10.3. Space plane fitting by range sensing. 10.4. Space plane fitting by stereo vision. 11. 3-D Motion Analysis. 11.1. General theory. 11.2. Lincarization and renormalization. 11.3. Optimal correction and decomposition. 11.4. Reliability of 3-D reconstruction. 11.5. Critical surfaces. 11.6. 3-D reconstruction from planar surface motion. 11.7. Camera rotation and information. 12. 3-D Interpretation of Optical Flow. 12.1. Optical flow detection. 12.2. Theoretical basis of 3-D interpretation. 12.3. Optimal estimation of motion parameters. 12.4. Lincarization and renormalization. 12.5. Optimal 3-D reconstruction. 12.6. Reliability of 3-D reconstruction. 12.7. Critical surfaces for optical flow. 12.8. Analysis of planar surface optical flow. 12.9. Camera rotation and information. 13. Information Criterion for Model Selection. 13.1. Model selection criterion. 13.2. Mahalanobis geometry. 13.3. Expected residual. 13.4. Geometric information criterion. 13.5. 3-D reconstruction by stereo vision. 13.6. 3-D motion analysis. 13.7. 3-D interpretation of optical flow. 14. General Theory of Geometric Estimation. 14.1. Statistical estimation in engineering. 14.2. General geometric correction. 14.3. Maximum likelihood correction. 14.4. General parametric fitting. 14.5. Maximum likelihood fit. References. Index.
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