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.

3D Objects Face Clustering using Unsupervised Mean Shift

FARENZENA, Michela;CRISTANI, Marco;CASTELLANI, Umberto;FUSIELLO, Andrea
2007-01-01

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.
2007
9783905673623
Clustering; Computer Vision; Mean Shift
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/472377
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