Criterion Independent Hierarchical Segmentation for Unstructured 3D Datasets - Application to Range Images - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Accéder directement au contenu
Communication Dans Un Congrès Année : 2007

Criterion Independent Hierarchical Segmentation for Unstructured 3D Datasets - Application to Range Images

Résumé

We present a method for the segmentation of unstructured and unfiltered 3D data. The core of this approach is based on the construction of a local neighborhood structure and its recursive subdivision. 3D points will be organized into groups according to their spatial proximity, but also to their similarity in the attribute space. Thanks to this hierarchical approach, this method provides high flexibility to the final level of segmentation. We assume that the 3D image is composed of regions homogeneous according to some criterion (color, curvature, etc.), but no assumption about noise, nor spatial repartition/shape of the regions or points is made. Thus, this approach can be applied to a wide variety of segmentation problems, unlike most existing specialized methods. We demonstrate the performance of our algorithm with experimental results on real range images.
Fichier principal
Vignette du fichier
caguiar_IROS2007.pdf (499.21 Ko) Télécharger le fichier

Dates et versions

lirmm-00200011 , version 1 (20-12-2007)

Identifiants

  • HAL Id : lirmm-00200011 , version 1

Citer

Carla Aguiar, Sébastien Druon, André Crosnier. Criterion Independent Hierarchical Segmentation for Unstructured 3D Datasets - Application to Range Images. IROS'07: Intelligent Robots and Systems, Nov 2007, pp.XXX-YYY. ⟨lirmm-00200011⟩
158 Consultations
266 Téléchargements

Partager

Gmail Facebook X LinkedIn More