Data Segmentation and Model Selection for Computer Vision
A Statistical Approach
(Sprache: Englisch)
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...
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Klappentext zu „Data Segmentation and Model Selection for Computer Vision “
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.
Inhaltsverzeichnis zu „Data Segmentation and Model Selection for Computer Vision “
- Introductory Notes on the Nature of Model Based Data Segmentation- Robust Regression Methods and Model Selection
- 2D and 3D Scene Segmentation
- Model Selection Criteria for Geometric Inference
- Robust Data Segmentation for Computer Vision
- Robust Model Selection for 2 View Geometry
- Robust Mallows' Cp for Model Selection
Bibliographische Angaben
- 2000, 2000, 208 Seiten, Maße: 15,7 x 23,5 cm, Gebunden, Englisch
- Ed. by Alireza Bab-Hadiashar and David Suter
- Herausgegeben: David Suter, Alireza Bab-Hadiashar
- Verlag: Springer, New York
- ISBN-10: 0387988157
- ISBN-13: 9780387988153
- Erscheinungsdatum: 28.02.2000
Sprache:
Englisch
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