Statistical Optimization for Geometric Computation

Theory and Practice

By: KENICHI KANATANI

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Paperback | 26 July 2005

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528 Pages

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20.9 x 13.6 x 3.18

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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. 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.

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Image Processing Mathematics Reference, Information & Interdisciplinary Subjects Encyclopaedias & Reference Works Reference Works

Introduction	p. 1
Fundamentals of Linear Algebra	p. 27
Probabilities and Statistical Estimation	p. 61
Representation of Geometric Objects	p. 95
Geometric Correction	p. 131
3-D Computation by Stereo Vision	p. 171
Parametric Fitting	p. 209
Optimal Filter	p. 247
Renormalization	p. 267
Applications of Geometric Estimation	p. 295
3-D Motion Analysis	p. 325
3-D Interpretation of Optical Flow	p. 369
Information Criterion for Model Selection	p. 415
General Theory of Geometric Estimation	p. 451
References	p. 485
Index	p. 501
Table of Contents provided by Blackwell. All Rights Reserved.

Statistical Optimization for Geometric Computation

Theory and Practice

At a Glance

Paperback

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