Issue No. 12 - December (2011 vol. 33)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2011.85
Umberto Castellani , University of Verona, Verona
Marco Cristani , University of Verona, Verona and Istituto Italiano di Tecnologia, Genova
Vittorio Murino , University of Verona, Verona and Istituto Italiano di Tecnologia, Genova
In this paper, we propose a new approach for surface representation. Generative models are exploited for encoding the variations of local geometric properties of 3D shapes. Surfaces are locally modeled as a stochastic process which spans a neighborhood area through a set of circular geodesic pathways, captured by a modified version of a Hidden Markov Model (HMM) named multicircular HMM (MC-HMM). The approach proposed consists of two main phases: 1) local geometric feature collection and 2) MC-HMM parameter estimation. The effectiveness of our proposal is demonstrated by several applicative scenarios, all using well-known benchmark data sets, such as multiple view registration, matching of deformable shapes, and object recognition on cluttered scenes. The results achieved are very promising and open up the use of generative models as geometric descriptors in an extensive range of applications.
3D shape analysis, shape representation, Hidden Markov Models, generative modeling.
V. Murino, U. Castellani and M. Cristani, "Statistical 3D Shape Analysis by Local Generative Descriptors," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 33, no. , pp. 2555-2560, 2011.