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25 Remote Warehouse Operating Systems- General

Statistical Optimization for Geometric Computation: Theory and Practice

by

Statistical Optimization for Geometric Computation: Theory and Practice Cover

 

Synopses & Reviews

Publisher Comments:

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.

Synopsis:

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.

Synopsis:

Appropriate for graduate students, this text explains the foundations of statistical inference for building three-dimensional models with image and sensor data containing noise.

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

Product Details

ISBN:
9780486443089
Author:
Kanatani, Kenichi
Publisher:
Dover Publications
Author:
Mathematics
Author:
Kenichi Kan
Author:
atani
Subject:
General
Subject:
Reference
Subject:
Robotics
Subject:
Mathematical statistics
Subject:
Data Processing - Optical Data Processing
Subject:
Optical data processing
Subject:
Operating Systems - General
Edition Description:
Trade Paper
Series:
Dover Books on Mathematics
Publication Date:
20050731
Binding:
TRADE PAPER
Language:
English
Illustrations:
Y
Pages:
526
Dimensions:
8.5 x 5.38 in 1.23 lb

Related Subjects

Computers and Internet » Artificial Intelligence » Image Processing
Computers and Internet » Operating Systems » General
Science and Mathematics » Geology » Geophysics
Science and Mathematics » Mathematics » General
Science and Mathematics » Mathematics » Reference
Science and Mathematics » Physics

Statistical Optimization for Geometric Computation: Theory and Practice New Trade Paper
0 stars - 0 reviews
$26.95 In Stock
Product details 526 pages Dover Publications - English 9780486443089 Reviews:
"Synopsis" by ,
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.

"Synopsis" by ,
Appropriate for graduate students, this text explains the foundations of statistical inference for building three-dimensional models with image and sensor data containing noise.

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