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Issue No.12 - December (2011 vol.33)
pp: 2555-2560
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
ABSTRACT
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.
INDEX TERMS
3D shape analysis, shape representation, Hidden Markov Models, generative modeling.
CITATION
Umberto Castellani, Marco Cristani, Vittorio Murino, "Statistical 3D Shape Analysis by Local Generative Descriptors", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.33, no. 12, pp. 2555-2560, December 2011, doi:10.1109/TPAMI.2011.85
REFERENCES
[1] P. Bariya and K. Nishino, "Scale-Hierarchical 3d Object Recognition in Cluttered Scenes," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, pp. 1657-1664, 2010.
[2] P. Besl and H. McKay, "A Method for Registration of 3-D Shapes," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 14, no. 2, pp. 239-256, Feb. 1992.
[3] A.M. Bronstein, M.M. Bronstein, and R. Kimmel, "Efficient Computation of Isometry-Invariant Distances Between Surfaces," SIAM J. Scientific Computing, vol. 28, no. 5, pp. 1812-1836, 2006.
[4] A.M. Bronstein, M.M. Bronstein, and R. Kimmel, Numerical Geometry of Non-Rigid Shapes. Springer Verlag, 2007.
[5] M.M. Bronstein and A.M. Bronstein, "Shape Recognition with Spectral Distances," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 33, no. 5, pp. 1065-1071, May 2011.
[6] U. Castellani, M. Cristani, S. Fantoni, and V. Murino, "Sparse Points Matching by Combining 3D Mesh Saliency with Statistical Descriptors," Computer Graphics Forum, vol. 27, pp. 643-652, 2008.
[7] R. Duda, P. Hart, and D. Stork, Pattern Classification. Wiley, 2001.
[8] M. Fischler, "Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography," Comm. ACM, vol. 24, no. 6, pp. 381-395, 1981.
[9] R. Huang, V. Pavlovic, and D.N. Metax, "Embedded Profile Hidden Markov Models for Shape Analysis," Proc. IEEE 11th Int'l Conf. Computer Vision, pp. 1-8, 2007.
[10] D. Huber and M. Hebert, "Fully Automatic Registration of Multiple 3D Data Sets," Image and Vision Computing, vol. 21, no. 7, pp. 637-650, 2003.
[11] A. Johnson and M. Hebert, "Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 5, pp. 433-449, May 1999.
[12] D. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," Int'l J. Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.
[13] A. Mian, M. Bennamoun, and R. Owens, "A Novel Representation and Feature Matching Algorithm for Automatic Pairwise Registration of Range Images," Int'l J. Computer Vision, vol. 66, no. 1, pp. 19-40, 2006.
[14] J. Novatnack and K. Nishino, "Scale-Dependent 3D Geometric Features," Int'l Conf. Computer Vision, 2007.
[15] J. Novatnack and K. Nishino, "Scale-Dependent/Invariant Local 3D Shape Descriptors for Fully Automatic Registration of Multiple Sets of Range Images," Proc. 10th European Conf. Computer Vision: Part III, 2008.
[16] S. Petitjean, "A Survey of Methods for Recovering Quadrics in Triangle Meshes," ACM Computing Surveys, vol. 34, no. 2, pp. 211-262, 2002.
[17] L. Rabiner, "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition," Proc. IEEE, vol. 77, no. 2, pp. 257-286, Feb. 1989.
[18] P. Smyth, "Clustering Sequences with Hidden Markov Models," Advances in Neural Information Processing Systems, pp. 648-654, 1997.
[19] J.W. Tangelder and R.C. Veltkamp, "A Survey of Content Based 3D Shape Retrieval Methods," Proc. Int'l Conf. Shape Modeling and Applications, 2004.
[20] O. van Kaick, H. Zhang, G. Hamarneh, and D. Cohen-Or, "A Survey on Shape Correspondence," Proc. Conf. EuroGraphics: State-of-the-Art Report, pp. 1-23, 2010.
[21] A. Zaharescu, E. Boyer, K. Varanasi, and R. Horaud, "Surface Feature Detection and Description with Applications to Mesh Matching," Proc. Int'l Conf. Computer Vision and Pattern Recognition, pp. 373-380, 2009.
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