Generalized Image Models and Their Application as Statistical Models of Images - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2003

Generalized Image Models and Their Application as Statistical Models of Images

Résumé

A generalized image model (GIM) is presented. Images are represented as sets of 4-dimensional sites combining position and intensity information, as well as their associated uncertainty and joint variation. This model seamlessly allows for the representation of both images and statistical models, as well as other representations such as landmarks or meshes. A GIM-based registration method aimed at the construction and application of statistical models of images is proposed. A procedure based on the iterative closest point (ICP) algorithm is modified to deal with features other than position and to integrate statistical information. Furthermore, we modify the ICP framework by using a Kalman filter to efficiently compute the transformation. The initialization and update of the statistical model are also described.
Fichier principal
Vignette du fichier
GonzalezBallester_MICCAI03.pdf (188.82 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03989511 , version 1 (14-02-2023)

Identifiants

Citer

Miguel Ángel González Ballester, Xavier Pennec, Nicholas Ayache. Generalized Image Models and Their Application as Statistical Models of Images. MICCAI 2003 - Medical Image Computing and Computer-Assisted Intervention, Nov 2003, Montréal Québec, Canada. pp.150-157, ⟨10.1007/978-3-540-39903-2_19⟩. ⟨hal-03989511⟩

Collections

INRIA INRIA2
12 Consultations
13 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More