Description: Statistical Optimization For Geometric Computation : Theory And Practice, Paperback by Kanatani, Kenichi, ISBN 0486443086, ISBN-13 9780486443089, Like New Used, Free shipping in the US 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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Book Title: Statistical Optimization For Geometric Computation : Theory And P
Number of Pages: 528 Pages
Publication Name: Statistical Optimization for Geometric Computation : Theory and Practice
Language: English
Publisher: Dover Publications, Incorporated
Subject: Optical Data Processing, Référence, Probability & Statistics / General
Item Height: 1.1 in
Publication Year: 2005
Item Weight: 19.2 Oz
Type: Textbook
Item Length: 8.4 in
Subject Area: Mathematics, Computers
Author: Kenichi Kanatani
Series: Dover Books on Mathematics Ser.
Item Width: 5.4 in
Format: Perfect
