Data Segmentation and Model Selection for Computer Vision: A Statistical Approach
Author:   Alireza Bab-Hadiashar ,  David Suter ,  Alireza Bab-Hadiashar ,  David Suter
Publisher:   Springer-Verlag New York Inc.
Edition:   2000 ed.
ISBN:  

9780387988153


Pages:   208
Publication Date:   28 February 2000
Format:   Hardback
Availability:   In Print   Availability explained
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Data Segmentation and Model Selection for Computer Vision: A Statistical Approach


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Overview

The problem of range and motion segmentation is of major importance in computer vision, image procession, and intelligent robotics. This edited volume explores several issues relating to parametric segmentation including robust operations, model selection criteria and automatic model selection, and 2D and 3D scene segmentation. Emphasis is placed on robust model selection with techniques such as robust Mallows Cp, least K-th order statistical model fitting (LKS), and robust regression receiving much attention. With contributions from leading researchers, this book is a valuable resource for researchers and graduate students working in computer vision, pattern recognition, image processing, and robotics.

Full Product Details

Author:   Alireza Bab-Hadiashar ,  David Suter ,  Alireza Bab-Hadiashar ,  David Suter
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   2000 ed.
Dimensions:   Width: 15.50cm , Height: 1.40cm , Length: 23.50cm
Weight:   1.120kg
ISBN:  

9780387988153


ISBN 10:   0387988157
Pages:   208
Publication Date:   28 February 2000
Audience:   College/higher education ,  General/trade ,  Professional and scholarly ,  Postgraduate, Research & Scholarly ,  General
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

I Historical Review.- 1 2D and 3D Scene Segmentation for Robotic Vision.- II Statistical and Geometrical Foundations.- 2 Robust Regression Methods and Model Selection.- 3 Robust Measures of Evidence for Variable Selection.- 4 Model Selection Criteria for Geometric Inference.- III Segmentation and Model Selection: Range and Motion.- 5 Range and Motion Segmentation.- 6 Model Selection for Structure and Motion Recovery from Multiple Images.- References.

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