OATAO - Open Archive Toulouse Archive Ouverte Open Access Week

How to Build an Average Model When Samples Are Variably Incomplete? Application to Fossil Data

Dumoncel, Jean and Subsol, Gérard and Durrleman, Stanley and Jessel, Jean-Pierre and Beaudet, Amélie and Braga, José How to Build an Average Model When Samples Are Variably Incomplete? Application to Fossil Data. (2016) In: 7th International Workshop on Biomedical Image Registration (WBIR 2016) in IEEE Conference on Computer Vision and Patterne Recognition Workshop (CVPRW 2016), 26 June 2016 - 1 July 2016 (Las Vegas, United States).

Full text not available from this repository.

Official URL: http://dx.doi.org/10.1109/CVPRW.2016.74


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 tissues (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.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to IEEE editor. The definitive version is available at http://ieeexplore.ieee.org This papers appears in Proceedings of CVPRW 2016. Electronic ISBN: 978-1-5090-1437-8 The original PDF of the article can be found at: https://ieeexplore.ieee.org/document/7789564/ Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
French research institutions > Institut National de la Recherche en Informatique et en Automatique - INRIA (FRANCE)
Other partners > Université Pierre et Marie Curie, Paris 6 - UPMC (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Université de Montpellier 2 (FRANCE)
Other partners > University of Pretoria (SOUTH AFRICA)
Laboratory name:
HPC resources of CALMIP supercomputing center under the allocation 2016- [P1440] attributed to the AMIS laboratory - French Ministry of Foreign Affairs - CNRS (Centre National de la Recherche Scientifique)
Deposited On:18 Apr 2018 15:06

Repository Staff Only: item control page