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Statistical Optimization for Geometric Computation (Theory and Practice)
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Product Details
Author:
Kenichi Kanatani
Format:
Paperback
Pages:
526
Publisher:
Dover Publications (July 26, 2005)
Language:
English
ISBN-13:
9780486443089
ISBN-10:
0486443086
Weight:
19.28oz
Dimensions:
5.375" x 8.5"
Case Pack:
16
Series:
Dover Books on Mathematics
File:
Dover-Dover_08032024_P7614837_onix30_Complete-20240803.xml
Folder:
Dover
As low as:
$25.60
List Price:
$26.95
Publisher Identifier:
P-DOVER
Discount Code:
D
Audience:
College/higher education
Pub Discount:
65
Overview
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.
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.
