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Mathematics Statistical Optimization for Geometric Computation: Theory and Practice (Machine Intelligence and Pattern Recognition, Vol 18)

Posted on 2010-04-13




Name:Mathematics Statistical Optimization for Geometric Computation: Theory and Practice (Machine Intelligence and Pattern Recognition, Vol 18)
ASIN/ISBN:0444824278
Author:Kenichi Kanatani
Publisher:Elsevier Science Ltd (1996)
Language:English
Pages:Hardcover, 524 pages
File size:12.8 Mb
   Mathematics Statistical Optimization for Geometric Computation: Theory and Practice (Machine Intelligence and Pattern Recognition, Vol 18)


Author: Kenichi Kanatani


Publisher: Elsevier Science Ltd (1996)


Binding: Hardcover, 524 pages


pricer: $200.75


ISBN-10: 0444824278


editorialreviews

This book discusses mathematical foundations of statistical inference for building a 3-D model of the environment from image and sensor data that contain noise - a central task for autonomous robots guided by video cameras and sensors. A theoretical accuracy bound is derived for the optimization procedure for maximizing the reliability of the estimation based on noisy data, and practical computational schemes that attain that bound are derived. Many synthetic and real data examples are given to demonstrate that conventional methods are not optimal and how accuracy improves if truly optimal methods are employed.




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Mathematics Statistical Optimization for Geometric Computation: Theory and Practice

Mathematics Statistical Optimization for Geometric Computation: Theory and Practice

Kenichi Kanatani ¡°Statistical Optimization for Geometric Computation: Theory and Practice" Elsevier Science Ltd | 1996-05 | ISBN: 0444824278 | 524 pages | PDF | 19,7 Mb depositfiles.com easy-share ---No mirrors, please---

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