Multi-layer Ontologies for Integrated 3D Shape Segmentation and Annotation - Université Clermont Auvergne Access content directly
Book Sections Year : 2016

Multi-layer Ontologies for Integrated 3D Shape Segmentation and Annotation


Mesh segmentation and semantic annotation are used as preprocessing steps for many applications, including shape retrieval, mesh abstraction, and adap-tive simplification. In current practice, these two steps are done sequentially: a purely geometrical analysis is employed to extract the relevant parts, and then these parts are annotated. We introduce an original framework where annotation and seg-mentation are performed simultaneously, so that each of the two steps can take advantage of the other. Inspired by existing methods used in image processing, we employ an expert's knowledge of the context to drive the process while minimizing the use of geometric analysis. For each specific context a multi-layer ontology can be designed on top of a basic knowledge layer which conceptualizes 3D object features from the point of view of their geometry, topology, and possible attributes. Each feature is associated with an elementary algorithm for its detection. An expert can define the upper layers of the ontology to conceptualize a specific domain without the need to reconsider the elementary algorithms. This approach has a twofold advantage: on one hand it allows to leverage domain knowledge from experts even if they have limited or no skills in geometry processing and computer program-Thomas Dietenbeck Sorbonne Universités, UPMC Univ Paris 06, INSERM UMRS 1146, CNRS UMR 7371, Labora-toire d'Imagerie Biomédicale, F-75013,
Fichier principal
Vignette du fichier
dietenbeck.AKDM.pdf (5.15 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01619338 , version 1 (19-10-2017)



Thomas Dietenbeck, Fakhri Torkhani, Ahlem Othmani, Marco Attene, Jean-Marie Favreau. Multi-layer Ontologies for Integrated 3D Shape Segmentation and Annotation. Advances in Knowledge Discovery and Management, 2016, ⟨10.1007/978-3-319-45763-5_10⟩. ⟨hal-01619338⟩
811 View
292 Download



Gmail Facebook Twitter LinkedIn More