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3D Part Segmentation Using Simulated Electrical Charge Distributions
November 1997 (vol. 19 no. 11)
pp. 1223-1235

Abstract—A novel approach to 3D part segmentation is presented. It is a well-known physical fact that electrical charge on the surface of a conductor tends to accumulate at a sharp convexity and vanish at a sharp concavity. Thus, object part boundaries, which are usually denoted by a sharp surface concavity, can be detected by simulating the electrical charge density over the object surface and locating surface points which exhibit local charge density minima. Beginning with single- or multi-view range data of a 3D object, we simulate the charge density distribution over an object's surface which has been tessellated by a triangular mesh. We detect the deep surface concavities by tracing local charge density minima and then decompose the object into parts at these points. The charge density computation does not require an assumption on surface smoothness and uses weighted global data to produce robust local surface features for part segmentation.

[1] H. Asada and M. Brady, "The Curvature Primal Sketch," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 8, no. 1, pp. 2-14, Jan. 1986.
[2] R. Bajcsy, F. Solina, and A. Gupta, "Segmentation versus Object Representation-Are They Separable?" Analysis and Interpretation of Range Images, P. Jain and A. Jain, eds., pp. 207-223. Springer-Verlag, 1990.
[3] R. Barrett et al., Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods.Philadelphia: SIAM, 1994.
[4] P.J. Besl and R.C. Jain, "Invariant Surface Characteristics for Three Dimensional Object Recognition in Range Images," Computer Vision, Graphics, and Image Processing, vol. 33, no. 1, pp. 33-88, 1986.
[5] I. Biederman, "Human Image Understanding: Recent Research and a Theory," Computer Vision, Graphics, and Image Processing, vol. 32, pp. 29-73, 1985.
[6] F.J. Bueche, Introduction to Physics for Scientists and Engineers, third edition. New York: McGraw-Hill, 1980.
[7] J. Canny, "A Computational Approach to Edge Detection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679-698, Nov. 1986.
[8] Y. Chen and G. Medioni, “Surface Description of Complex Objects from Multiple Range Images,” Proc. Conf. Computer Vision and Pattern Recognition, pp. 153-158, June 1994.
[9] D. DeCarlo and D. Metaxas, "Adaptive Shape Evolution Using Blending," IEEE Proc. Int'l Conf. Computer Vision, pp. 834-839, 1995.
[10] S.J. Dickinson, D. Metaxas, and A. Pentland, "Constrained Recovery of Deformable Models from Range Data," Proc. Int'l Workshop Visual Form,Capri, Italy, May 1994.
[11] S.J. Dickinson, A.P. Pentland, and A. Rosenfeld, "3D Shape Recovery Using Distributed Aspect Matching," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 14, no. 2, pp. 174-198, Feb. 1992.
[12] O.D. Faugeras, M. Hebert, P. Mussi, and J.D. Boissonnat, "Polyhedral Approximation of 3-D Objects without Holes," Computer Vision, Graphics, and Images Processing, vol. 25, pp. 169-183, 1984.
[13] F.P. Ferrie, J. Lagarde, and P. Whaite, "Darboux Frames, Snakes and Superquadrics: Geometry from the Bottom Up," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 8, pp. 771-784, Aug. 1993.
[14] F.P. Ferrie and M.D. Levine, "Deriving Coarse 3D Models of Objects," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 345-353,Ann Arbor, Mich., June 1988.
[15] H. Freeman, "Shape Descriptions via the Use of Critical Points," Pattern Recognition, vol. 10, no. 3, pp. 159-166, 1978.
[16] P.J. Giblin, Graphs, Surfaces and Homology, second edition. Chapman and Hall Ltd, 1981.
[17] T. Grigorishin, G. Abdel-Hamid, and Y.H. Yang, "Skeletonization: An Electrostatic Field-Based Approach," technical report, Computer Vision Laboratory, Dept. of Computer Science, Univ. of Saskatchewan, Saskatoon, Saskatchewan, Canada, Feb. 1996.
[18] V. Guillemin and A. Pollack, Differential Topology.Englewood Cliffs, N.J.: Prentice Hall, 1974.
[19] A. Gupta and R. Bajcsy, "Volumetric Segmentation of Range Images of 3D Objects Using Superquadric Models," Computer Vision, Graphics, and Image Processing: Image Understanding, vol. 58, no. 3, pp. 302-326, Nov. 1993.
[20] D. Hoffman and W. Richards, "Parts of Recognition," Cognition, vol. 18, pp. 65-96, 1984.
[21] R. Hoffman and A.K. Jain, "Segmentation and Classification of Range Images," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 9, no. 5, pp. 608-620, 1987.
[22] T. Horikoshi and S. Suzuki, "3D Parts Decomposition from Sparse Range Data Using Information Criterion," Proc. 1993 IEEE Conf. Computer Vision and Pattern Recognition, pp. 168-173,New York, June 1993.
[23] J.D. Jackson, Classical Electrodynamics.New York: Wiley, 1975.
[24] J.J. Koenderink and A.J. Van Doorn, "The Shape of Smooth Objects and the Way Contours End," Perception, vol. 11, pp. 129-137, 1982.
[25] A. Lejeune and F. Ferrie, "Partioning Range Images Using Curvature and Scale," Proc. 1993 IEEE Conf. Computer Vision and Pattern Recognition, pp. 800-801,New York, June 1993.
[26] R.G. Lerner and G.L. Trigg, Encyclopedia of Physics, second edition. New York: VCH Publishers, 1991.
[27] M. Leyton, "Inferring Causal History from Shape," Cognitive Science, vol. 13, pp. 357-389, 1989.
[28] R. Mohr and R. Bajcsy, "Packing Volumes by Spheres," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 5, pp. 111-116, 1983.
[29] Q.L. Nguyen and M.D. Levine, "Representing 3-D Objects in Range Images Using Geons," Computer Vision and Image Understanding, vol. 63, no. 1, pp. 158-168, Jan. 1996.
[30] B. O'Neill, Elementary Differential Geometry.New York and London: Academic Press, 1966.
[31] J. O'Rourke and N. Badler, "Decomposition of Three-Dimensional Objects into Spheres," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 1, pp. 295-305, 1979.
[32] A. Pentland, "Part Segmentation for Object Recognition," Neural Computation, vol. 1, pp. 82-91, 1989.
[33] A.P. Pentland, "Recognition by Parts," Proc. First Int'l Conf. Computer Vision, pp. 8-11,London, June 1987.
[34] T. Phillips, R. Cannon, and A. Rosenfeld, "Decomposition and Approximation of 3-D Solids," Computer Vision, Graphics, and Image Processing, vol. 33, pp. 307-317, 1986.
[35] R.H. Price and R.J. Crowley, "The Lightning-Rod Fallacy," Am. J. Physics, vol. 53, no. 9, pp. 843-848, Sept. 1985.
[36] N.S. Raja and A.K. Jain, "Obtaining Generic Parts from Range Data Using a Multi-View Representation," Computer Vision, Graphics, and Image Processing: Image Understanding, vol. 60, no. 1, pp. 44-64, July 1994.
[37] H. Rom and G. Medioni, "Part Decomposition and Description of 3D Shapes," Proc. 12th Int'l Conf. Pattern Recognition, vol. I, pp. 629-632,Jerusalem, Oct. 1994.
[38] R.A. Serway, Physics for Sciences&Engineers, second edition. Saunders College Publishing, 1986.
[39] H. Shum et al., "An Integrated Approach to Free-Formed Object Modeling," Proc. IEEE Int'l Conf. Computer Vision, pp. 870-875, 1995.
[40] K. Siddiqi, K.J. Tresness, and B.B. Kimia, "Parts of Visual Form: Psychophysical Aspects," Perception, vol. 25, no. 4, pp. 399-424, Apr. 1994.
[41] P.P. Silvester and R.L. Ferrari, Finite Elements for Electrical Engineering, second edition. Cambridge, U.K.: Cambridge Univ. Press, 1990.
[42] W.R. Smythe, Static and Dynamic Electricity, third edition, p. 124.New York: McGraw-Hill, 1968.
[43] F. Solina, A. Leonardis, and A. Macerl, "A Direct Part-Level Segmentation of Range Images Using Volumetric Models," Proc. 1994 IEEE Int'l Conf. Robotics and Automation, pp. 2,254-2,259,San Diego, Calif., May 1994.
[44] B.I. Soroka, R.L. Andesson, and R.K. Bajcsy, "Generalised Cylinders from Local Aggregation of Sections," Pattern Recognition, vol. 13, no. 5, pp. 353-363, 1981.
[45] M. Soucy and D. Laurendeau, "A General Surface Approach to the Integration of a Set of Range Views," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 17, no. 4, pp. 344-358, Apr. 1995.
[46] E. Trucco, "Part Segmentation of Slice Data Using Regularity," Signal Processing, vol. 32, pp. 73-90, 1993.
[47] E. Trucco and R.B. Fisher, "Experiments in Curvature-Based Segmentation of Range Data," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 17, no. 2, pp. 177-181, Feb. 1995.
[48] D. Wilton et al., "Potential Integrals for Uniform and Linear Source Distributions on Polygonal and Polyhedral Domains," IEEE Trans. Antennas and Propagation, vol. 32, no. 3, pp. 276-281, 1984.
[49] K. Wu, "Computing Parametric Geon Descriptions of 3D Multi-Part Objects," PhD thesis, McGill Univ., Montreal, Quebec, Canada, Apr. 1996.
[50] K. Wu and M.D. Levine, "3-D Shape Approximation Using Parametric Geons," Image and Vision Computing, vol. 15, pp. 143-158, 1997.
[51] Y. Yacoob and L.S. Davis, "Labeling of Human Face Components from Range Data," Computer Vision, Graphics, and Image Processing: Image Understanding, vol. 60, no. 2, pp. 168-178, Sept. 1994.

Index Terms:
Computer vision, 3D, range data, shape, part segmentation, physics-based vision, electrical charge density distribution, finite element, surface triangulation, surface characterization.
Citation:
Kenong Wu, Martin D. Levine, "3D Part Segmentation Using Simulated Electrical Charge Distributions," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 11, pp. 1223-1235, Nov. 1997, doi:10.1109/34.632982
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