Joint Color-Spatial-Directional clustering and Region Merging (JCSD-RM) for unsupervised RGB-D image segmentation

Abul Hasnat 1 Olivier Alata 1 Alain Treméau 1
1 LHC - Laboratoire Hubert Curien [Saint Etienne]
Abstract : Recent advances in depth imaging sensors provide easy access to the synchronized depth with color, called RGB-D image. In this paper, we propose an unsupervised method for indoor RGB-D image segmentation and analysis. We consider a statistical image generation model based on the color and geometry of the scene. Our method consists of a joint color-spatial-directional clustering method followed by a statistical planar region merging method. We evaluate our method on the NYU depth database and compare it with existing unsupervised RGB-D segmentation methods. Results show that, it is comparable with the state of the art methods and it needs less computation time. Moreover, it opens interesting perspectives to fuse color and geometry in an unsupervised manner.
Keywords : Unsupervised Clustering RGB-D image segmentation Directional distributions Bregman divergence Mixture model Region adjacency graph Region merging
Type de document :
Article dans une revue
IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2016, 〈10.1109/TPAMI.2015.2513407〉
Liste complète des métadonnées

Littérature citée [44 références]  Voir  Masquer  Télécharger
https://hal-ujm.archives-ouvertes.fr/ujm-01273670
Contributeur : Olivier Alata <>
Soumis le : vendredi 12 février 2016 - 17:32:26
Dernière modification le : jeudi 26 juillet 2018 - 01:10:43
Document(s) archivé(s) le : samedi 12 novembre 2016 - 19:58:59

Fichier

pami_jcsd_rm_rev_draft_2.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

UNIV-ST-ETIENNE | IOGS | PARISTECH | UNIV-PARIS-SACLAY

Citation

Abul Hasnat, Olivier Alata, Alain Treméau. Joint Color-Spatial-Directional clustering and Region Merging (JCSD-RM) for unsupervised RGB-D image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2016, 〈10.1109/TPAMI.2015.2513407〉. 〈ujm-01273670〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

115

Téléchargements de fichiers

121

Contact

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