How to build an average model when samples are variably incomplete? Application to fossil data

Jean Dumoncel 1 Gérard Subsol 2 Stanley Durrleman 3 Jean Pierre Jessel 4 Amélie Beaudet 1 José Braga 1
1 AMIS - Anthropologie Moléculaire et Imagerie de Synthèse
2 ICAR - Image & Interaction
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
3 ARAMIS - Algorithms, models and methods for images and signals of the human brain
Inria Paris-Rocquencourt, UPMC - Université Pierre et Marie Curie - Paris 6, ICM - Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute
4 IRIT - Institut de recherche en informatique de Toulouse
Abstract : In paleontology, incomplete samples with small or large missing parts are frequently encountered. For example, dental crowns, which are widely studied in paleontology because of their potential interest in taxonomic and phylogenetic analyses, are nearly systematically affected by a variable degree of wear that alters considerably their shape. It is then difficult to compute a significant reference surface model based on classical methods which are used to build atlases from set of samples. In this paper, we present a general approach to deal with the problem of estimating an average model from a set of incomplete samples. Our method is based on a state-of-the-art non-rigid surface registration algorithm. In a first step, we detect missing parts which allows one to focus only on the common parts to get an accurate registration result. In a second step, we try to build average model of the missing parts by using information which is available in a subset of the samples. We specifically apply our method on teeth, and more precisely on the surface in between dentine and enamel issues (EDJ). We investigate the robustness and accuracy properties of the methods on a set of artificial samples representing a high degree of incompleteness. We compare the reconstructed complete shape to a ground-truth dataset. We then show some results on real data.
Type de document :
Communication dans un congrès
WBIR: Workshop on Biomedical Image Registration, Jul 2016, Las Vegas, United States. 7th International Workshop on Biomedical Image Registration - IEEE Conference on Computer Vision and Patterne Recognition Workshop, pp.541-548, 2016, 〈http://wbir2016.doc.ic.ac.uk/〉
Liste complète des métadonnées

Littérature citée [25 références]  Voir  Masquer  Télécharger
https://hal-lirmm.ccsd.cnrs.fr/lirmm-01381310
Contributeur : Gérard Subsol <>
Soumis le : vendredi 14 octobre 2016 - 12:31:31
Dernière modification le : mercredi 12 septembre 2018 - 17:46:03

Fichier

Dumoncel_How_to_Build_CVPR_201...
Fichiers éditeurs autorisés sur une archive ouverte

Identifiants

  • HAL Id : lirmm-01381310, version 1

Collections

LIRMM | UPMC | ICM | INRIA | ICARLIRMM | UNIV-TLSE3 | SMS | UNIV-TLSE2 | ARAMISLAB | ARAMILAB | MIPS | UNIV-MONTPELLIER

Citation

Jean Dumoncel, Gérard Subsol, Stanley Durrleman, Jean Pierre Jessel, Amélie Beaudet, et al.. How to build an average model when samples are variably incomplete? Application to fossil data. WBIR: Workshop on Biomedical Image Registration, Jul 2016, Las Vegas, United States. 7th International Workshop on Biomedical Image Registration - IEEE Conference on Computer Vision and Patterne Recognition Workshop, pp.541-548, 2016, 〈http://wbir2016.doc.ic.ac.uk/〉. 〈lirmm-01381310〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

349

Téléchargements de fichiers

237

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD