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Issue No.05 - May (2011 vol.33)
pp: 1058-1064
Yonghuai Liu , Aberystwyth University, Ceredigion
For accurate registration of overlapping free form shapes, different points in one shape must select different points in another as their most sensible correspondents. To reach this ideal state, in this paper we develop a novel algorithm to penalize those points in one shape that select the same closest point in another as their tentative correspondents. The novel algorithm then models the relative weight change over time of a tentative correspondence as the difference between the negative functions of the numbers of points in one shape that actually and ideally select the same closest point in another. Such modeling results in an optimal estimation of the weights of different tentative correspondences, in the sense of deterministic annealing, that lead the camera motion parameters to be estimated in the weighted least squares sense. The proposed algorithm is initialized using the pure translational motion derived from the centroids difference of the overlapping free form shapes being registered. Experimental results show that it outperforms three selected state-of-the-art algorithms on the whole for the accurate and robust registration of real overlapping free form shapes captured using two different laser scanners under typical imaging conditions.
Tentative correspondence, closest point sharing, penalization, weight, deterministic annealing, accurate and robust registration, overlapping free form shapes.
Yonghuai Liu, "Penalizing Closest Point Sharing for Automatic Free Form Shape Registration", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.33, no. 5, pp. 1058-1064, May 2011, doi:10.1109/TPAMI.2010.207
[1] P.J. Besl and N.D. McKay, “A Method for Registration of 3D Shapes,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 14, no. 2, pp. 239-256, Feb. 1992.
[2] G. Dewaele, F. Devernay, and R. Horaud, “Hand Motion from 3D Point Trajectories and a Smooth Surface Model,” Proc. European Conf. Computer Vision, pp. 495-507, 2004.
[3] R.A. Fisher, The Genetical Theory of Natural Selection. Clarendon Press, 1930.
[4] S. Gold et al., “New Algorithms for 2-D and 3-D Point Matching: Pose Estimation and Correspondence,” Pattern Recognition, vol. 31, pp. 1019-1031, 1998.
[5] D. Huber and M. Hebert, “Fully Automatic Registration of Multiple 3D Data Sets,” Image and Vision Computing, vol. 21, pp. 637-650, 2003.
[6] S. Khoualed, U. Castellani, and A. Bartoli, “Semantic Shape Context for the Registration of Multiple Partial 3D Views,” Proc. British Machine Vision Conf., 2009.
[7] Y. Liu, “Automatic Registration of Overlapping 3D Point Clouds Using Closest Points,” Image and Vision Computing, vol. 24, pp. 762-781, 2006.
[8] Y. Liu, “A Mean Field Annealing Approach to Accurate Free Form Shape Matching,” Pattern Recognition, vol. 40, pp. 2418-2436, 2007.
[9] Y. Liu, “Constraints for Closest Point Finding,” Pattern Recognition Letters, vol. 29, pp. 841-851, 2008.
[10] K.-L. Low and A. Lastra, “Reliable and Rapidly-Converging ICP Algorithm Using Multiresolution Smoothing,” Proc. Fourth Int'l Conf. 3-D Digital Imaging and Modeling, pp. 171-178, 2003.
[11] T. Masuda, “Log-Polar Height Maps for Multiple Range Image Registration,” Computer Vision and Image Understanding, vol. 113, pp. 1158-1169, 2009.
[12] OSU (MSU/WSU) range image database, RIDindex.htm, 2010.
[13] J.M. Phillips et al., “Outlier Robust ICP for Minimizing Fractional RMSD,” Proc. Sixth Int'l Conf. 3-D Digital Imaging and Modeling, pp. 427-434, 2007.
[14] J. Puzicha et al., “Deterministic Annealing: Fast Physical Heuristics for Real-Time Optimisation of Large Systems,” Proc. 15th IMACS World Conf. Scientific Computation, Modelling and Applied Math., vol. VI, pp. 445-450, 1997.
[15] S. Rusinkiewicz and M. Levoy, “Efficient Variants of the ICP Algorithm,” Proc. Third Int'l Conf. 3-D Digital Imaging and Modeling, pp. 145-152, 2001.
[16] J. Santamaria et al., “A Scatter Search-Based Technique for Pair-Wise 3D Range Image Registration in Forensic Anthropology,” Soft Computing, vol. 11, pp. 819-828, 2007.
[17] R. Sara, I.S. Okatani, and A. Sugimoto, “Globally Convergent Range Image Registration by Graph Kernel Algorithm,” Proc. Fifth Int'l Conf. 3-D Digital Imaging and Modeling, pp. 377-384, 2005.
[18] C. Schutz, T. Jost, and H. Hugli, “Multi-Feature Matching Algorithm for Free-Form 3D Surface Registration,” Proc. 14th Int'l Conf. Pattern Recognition, pp. 982-984, 1998.
[19] G.C. Sharp, S.W. Lee, and D.K. Wehe, “ICP Registration Using Invariant Features,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 1, pp. 90-112, Jan. 2002.
[20] G.C. Sharp, S.W. Lee, and D.K. Wehe, “Maximum-Likelihood Registration of Range Images with Missing Data,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 1, pp. 120-130, Jan. 2008.
[21] L. Silva, O.R.P. Bellon, and K.L. Boyer, “Precision Range Image Registration Using a Robust Surface Interpenetration Measure and Enhanced Genetic Algorithms,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 5, pp. 762-776, May 2005.
[22] F. Wang, B.C. Vemuri, and A. Rangarajan, “Groupwise Point Pattern Registration Using a Novel CDF-Based Jensen-Shannon Divergence,” Proc. IEEE CS Conf. Computer Vision and Patter Recognition, pp. 1283-1288, 2006.
[23] S.M. Yamany and A.A. Farag, “Surface Signatures: An Orientation Independent Free-Form Surface Representation Scheme for the Purpose of Objects Registration and Matching,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 8, pp. 1105-1120, Aug. 2002.
[24] Z. Zhang, “Iterative Point Matching for Matching of Free-Form Curves,” Technical Report 1658, Institut Nat'l de Recherche en Informatique et en Automatique (INRIA), France, May 1992.
[25] L. Zhu et al., “Efficient Registration for Precision Inspection of Free-Form Shapes,” Int'l J. Advanced Manufacturing Technology, vol. 32, pp. 505-515, 2007.
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