Titre :	Data Segmentation and Model Selection for Computer Vision : a Statistical Approach
Type de document :	texte imprime
Auteurs :	Bab-Hadiashar, Alireza ; Suter, David
Editeur :	New York : Springer
Année de publication :	2000
Importance :	207p.
Présentation :	ill.
Format :	24 cm
ISBN/ISSN/EAN :	978-0-387-98815-3
Note générale :	Index
Langues :	Anglais
Mots-clés :	Computer  Robotic  Geometric  Segmentation
Résumé :	This edited volume explores several issues relating to parametric segmentation including robust operations, model selection criteria and automatic model selection, plus 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 is a valuable resource for researchers and graduated students working in computer vision, pattern recognition, image processing and robotics.
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Data Segmentation and Model Selection for Computer Vision : a Statistical Approach [texte imprime] / Bab-Hadiashar, Alireza ; Suter, David . - New York : Springer, 2000 . - 207p. : ill. ; 24 cm.
ISBN : 978-0-387-98815-3
Index
Langues : Anglais

Mots-clés :	Computer  Robotic  Geometric  Segmentation
Résumé :	This edited volume explores several issues relating to parametric segmentation including robust operations, model selection criteria and automatic model selection, plus 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 is a valuable resource for researchers and graduated students working in computer vision, pattern recognition, image processing and robotics.
Permalink :	./index.php?lvl=notice_display&id=9992