3D Objects Face Clustering using Unsupervised Mean Shift
| dc.contributor.author | Farenzena, M. | en_US |
| dc.contributor.author | Cristani, M. | en_US |
| dc.contributor.author | Castellani, U. | en_US |
| dc.contributor.author | Fusiello, A. | en_US |
| dc.contributor.editor | Raffaele De Amicis and Giuseppe Conti | en_US |
| dc.date.accessioned | 2014-01-27T16:25:48Z | |
| dc.date.available | 2014-01-27T16:25:48Z | |
| dc.date.issued | 2007 | en_US |
| dc.description.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. | en_US |
| dc.description.seriesinformation | Eurographics Italian Chapter Conference | en_US |
| dc.identifier.isbn | 978-3905673-62-3 | en_US |
| dc.identifier.uri | https://doi.org/10.2312/LocalChapterEvents/ItalChap/ItalianChapConf2007/039-043 | en_US |
| dc.publisher | The Eurographics Association | en_US |
| dc.subject | Categories and Subject Descriptors (according to ACM CCS): I.3.5 [Computational Geometry and Object Modeling]: | en_US |
| dc.title | 3D Objects Face Clustering using Unsupervised Mean Shift | en_US |
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