Statistical Optimization for Geometric Computation: Theory and Practice by Kenic

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Statistical Optimization for Geometric Computation by Kenichi Kanatani

This text discusses the mathematical foundations of statistical inference for building 3-dimensional models from image and sensor data that contain noise — a task involving autonomous robots guided by video cameras and sensors. The text employs a theoretical accuracy for the optimization procedure, which maximizes the reliability of estimations based on noise data. 1996 edition.

FORMAT
Paperback LANGUAGE
English CONDITION
Brand New Publisher Description

This text for graduate students discusses the mathematical foundations of statistical inference for building three-dimensional models from image and sensor data that contain noise--a task involving autonomous robots guided by video cameras and text employs a theoretical accuracy for the optimization procedure, which maximizes the reliability of estimations based on noise data. The numerous mathematical prerequisites for developing the theories are explained systematically in separate chapters. These methods range from linear algebra, optimization, and geometry to a detailed statistical theory of geometric patterns, fitting estimates, and model selection. In addition, examples drawn from both synthetic and real data demonstrate the insufficiencies of conventional procedures and the improvements in accuracy that result from the use of optimal methods.

Table of Contents

1. Introduction 2. Fundamentals of Linear Algebra 3. Probabilities and Statistical Estimation 4. Representation of Geometric Objects 5. Geometric Correction 6. 3-D Computation by Stereo Vision 7. Parametric Fitting 8. Optimal Filter 9. Renormalization 10. Applications of Geometric Estimation 11. 3-D Motion Analysis 12. 3-D Interpretation of Optical Flow 13. Information Criterion for Model Selection 14. General Theory of Geometric Estimation References Index

Long Description

This text for graduate students discusses the mathematical foundations of statistical inference for building three-dimensional models from image and sensor data that contain noise--a task involving autonomous robots guided by video cameras and sensors. The text employs a theoretical accuracy for the optimization procedure, which maximizes the reliability of estimations based on noise data. The numerous mathematical prerequisites for developing the theories are explained systematically in separate chapters, and examples drawn from both synthetic and real data demonstrate the improvements in accuracy that result from the use of optimal methods. 1996 ed.

Details ISBN0486443086 Author Kenichi Kanatani Short Title STATISTICAL OPTIMIZATION FOR G Language English ISBN-10 0486443086 ISBN-13 9780486443089 Media Book Format Paperback Year 2005 Imprint Dover Publications Inc. Place of Publication New York Country of Publication United States Subtitle Theory and Practice Series Dover Books on Mathematics DOI 10.1604/9780486443089 UK Release Date 2005-07-29 AU Release Date 2005-07-29 NZ Release Date 2005-07-29 Pages 528 Publisher Dover Publications Inc. Publication Date 2005-07-26 DEWEY 006.42015195 Illustrations Illustrations Audience General US Release Date 2005-07-26 Country of Origin US Product Class Description Computing: Textbooks & Study Guides
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