3D Objects Face Clustering using Unsupervised Mean Shift

Farenzena, M.; Cristani, M.; Castellani, U.; Fusiello, A.

3D Objects Face Clustering using Unsupervised Mean Shift

Files

039-043.pdf (382.96 KB)

Date

2007

Authors

Farenzena, M.
Cristani, M.
Castellani, U.
Fusiello, A.

Publisher

The Eurographics Association

Abstract

In this paper, a novel approach to face clustering is proposed. The aim is the completely unsupervised extraction of planes in a polygonal a mesh, obtained from a 3D reconstruction process. In this context, 3D coordinates points are inevitably affected by error, therefore resiliency is a primal concern in the analysis. The method is based on the Mean Shift clustering paradigm, devoted to separating modes of a multimodal non-parametric density, by using a kernel-based technique. A critical parameter, the kernel bandwidth size, is here automatically detected by following a well-accepted partition stability criterion. Experimental and comparative results on synthetic and real data validate the approach.

        @inproceedings{:10.2312/LocalChapterEvents/ItalChap/ItalianChapConf2007/039-043
,
booktitle = {Eurographics Italian Chapter Conference
},
editor = {Raffaele De Amicis and Giuseppe Conti
},
title = {{3D Objects Face Clustering using Unsupervised Mean Shift
}},
author = {Farenzena, M. and 
Cristani, M. and 
Castellani, U. and 
Fusiello, A.
},
year = {2007
},
publisher = {The Eurographics Association
},
ISBN = {978-3905673-62-3
},
DOI = {/10.2312/LocalChapterEvents/ItalChap/ItalianChapConf2007/039-043
}
}