Skip to Main content Skip to Navigation
Conference papers

Model based clustering for 3D directional features: application to depth image analysis

Abul Hasnat 1 Olivier Alata 1 A. Trémeau 1
1 LHC - Laboratoire Hubert Curien [Saint Etienne]
Abstract : Model based clustering (MBC) is a method that selects an op- timal clustering solution from a set of candidate solutions. In this paper, we propose a novel MBC method to cluster three dimensional directional features. We assume that the features are generated from a finite statistical mixture model based on the von Mises-Fisher (vMF) distribution. The core elements of our proposed method are: (a) generate a set of vMF Mixture Models (vMFMM) and (b) select the optimal model using a parsimony based approach with information criteria. We empirically validate our proposed method by applying it on simulated data. Next, we apply it to cluster image normals in order to perform depth image analysis.
Keywords : Model based clustering Model selection von Mises-Fisher distribution Mixture model Depth image analysis
Document type :
Conference papers
Complete list of metadata
https://hal-ujm.archives-ouvertes.fr/ujm-01005184
Contributor : Olivier Alata <>
Submitted on : Thursday, June 12, 2014 - 10:36:33 AM
Last modification on : Monday, January 13, 2020 - 5:46:04 PM

Identifiers

  • HAL Id : ujm-01005184, version 1

Collections

UNIV-ST-ETIENNE | CNRS | LAHC | IOGS | PARISTECH | UDL

Citation

Abul Hasnat, Olivier Alata, A. Trémeau. Model based clustering for 3D directional features: application to depth image analysis. IEEE International Conference on Image Processing (ICIP) 2014, Oct 2014, Paris, France. pp.1-5. ⟨ujm-01005184⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

546