The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.10 - October (2008 vol.30)
pp: 1771-1785
ABSTRACT
We propose a general approach using Laplacian Eigenmaps and a graphical model of the human body to segment 3D voxel data of humans into different articulated chains. In the bottom-up stage, the voxels are transformed into a high-dimensional (6D or less) Laplacian Eigenspace (LE) of the voxel neighborhood graph. We show that LE is effective at mapping voxels on long articulated chains to nodes on smooth 1D curves that can be easily discriminated, and prove these properties using representative graphs. We fit 1D splines to voxels belonging to different articulated chains such as the limbs, head and trunk, and determine the boundary between splines using the spline fitting error. A top-down probabilistic approach is then used to register the segmented chains, utilizing their mutual connectivity and individual properties. Our approach enables us to deal with complex poses such as those where the limbs form loops. We use the segmentation results to automatically estimate the human body models. While we use human subjects in our experiments, the method is fairly general and can be applied to voxel-based segmentation of any articulated object composed of long chains. We present results on real and synthetic data that illustrate the usefulness of this approach.
INDEX TERMS
Pattern Recognition, Image Processing and Computer Vision, Segmentation, Graph-theoretic methods, Region growing, partitioning
CITATION
Aravind Sundaresan, Rama Chellappa, "Model Driven Segmentation of Articulating Humans in Laplacian Eigenspace", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.30, no. 10, pp. 1771-1785, October 2008, doi:10.1109/TPAMI.2007.70823
REFERENCES
[1] L. Mündermann, D. Anguelov, S. Corazza, A. Chaudhari, and T.P. Andriacchi, “Validation of a Markerless Motion Capture System for the Calculation of Lower Extremity Kinematics,” Proc. 20th Congress Int'l Soc. of Biomechanics and 29th Ann. Meeting Am. Soc. Biomechanics, 2005.
[2] S. Corazza, L. Mündermann, and T.P. Andriacchi, “Lower Limb Kinematics through Model-Free Markerless Motion Capture,” Proc. 20th Congress Int'l Soc. of Biomechanics and 29th Ann. Meeting Am. Soc. Biomechanics, 2005.
[3] C.-W. Chu, O.C. Jenkins, and M.J. Mataric, “Markerless Kinematic Model and Motion Capture from Volume Sequences,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR '03), vol. 2, pp. 475-482, June 2003.
[4] I. Mikić, M. Trivedi, E. Hunter, and P. Cosman, “Human Body Model Acquisition and Tracking Using Voxel Data,” Int'l J. Computer Vision, vol. 53, no. 3, 2003.
[5] L. Mündermann, S. Corazza, and T. Andriacchi, “Accurately Measuring Human Movement Using Articulated ICP with Soft-Joint Constraints and a Repository of Articulated Models,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2007.
[6] A. Sundaresan and R. Chellappa, “Segmentation and Probabilistic Registration of Articulated Body Model,” Proc. 18th IEEE Int'l Conf. Pattern Recognition (ICPR '06), vol. 2, pp. 92-96, Aug. 2006.
[7] A. Sundaresan and R. Chellappa, “Multicamera Tracking of Articulated Human Motion Using Motion and Shape,” Proc. Seventh Asian Conf. Computer Vision (ACCV '06), vol. 2, pp. 131-140, Jan. 2006.
[8] D.M. Gavrila, “The Visual Analysis of Human Movement: A Survey,” Computer Vision and Image Understanding, vol. 73, no. 1, pp. 82-98, 1999.
[9] J. Aggarwal and Q. Cai, “Human Motion Analysis: A Review,” Computer Vision and Image Understanding, vol. 73, no. 3, pp. 428-440, 1999.
[10] T. Moeslund and E. Granum, “A Survey of Computer Vision-Based Human Motion Capture,” Computer Vision and Image Understanding, pp. 231-268, 2001.
[11] L. Sigal and M. Black, “HumanEva: Synchronized Video and Motion Capture Dataset for Evaluation of Articulated Human Motion,” Technical Report CS-06-08, Brown Univ., 2006.
[12] K. Rohr, Human Movement Analysis Based on Explicit Motion Models. Kluwer Academic Publishers, 1997.
[13] D. Ramanan and D.A. Forsyth, “Finding and Tracking People from the Bottom Up,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR '03), vol. 2, pp. 467-474, June 2003.
[14] X. Ren, A.C. Berg, and J. Malik, “Recovering Human Body Configurations Using Pairwise Constraints between Parts,” Proc. 10th IEEE Int'l Conf. Computer Vision (ICCV '05), vol. 1, pp. 824-831, Oct. 2005.
[15] G. Mori and J. Malik, “Estimating Human Body Configurations Using Shape Context Matching,” Proc. Seventh European Conf. Computer Vision (ECCV '02), pp. 666-680, 2002.
[16] I.A. Kakadiaris and D. Metaxas, “3D Human Body Model Acquisition from Multiple Views,” Proc. First Int'l Conf. Computer Vision (ICCV '95), pp. 618-623, June 1995.
[17] K. Cheung, S. Baker, and T. Kanade, “Shape-from-Silhouette of Articulated Objects and Its Use for Human Body Kinematics Estimation and Motion Capture,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR '03), vol. 1, pp. 77-84, June 2003.
[18] J. Carranza, C. Theobalt, M. Magnor, and H. Seidel, “Freeviewpoint Video of Human Actors,” ACM Trans. Graphics, vol. 22, no. 2, pp. 569-577, 2003.
[19] J.B. Tenenbaum, V. de Silva, and J.C. Langford, “A Global Geometric Framework for Nonlinear Dimensionality Reduction,” Science, vol. 290, no. 5500, pp. 2319-2323, 2000.
[20] D. Anguelov, D. Koller, H. Pang, and P. Srinivasan, “Recovering Articulated Object Models from 3D Range Data,” Proc. 20th Conf. Uncertainty in Artificial Intelligence (UAI '04), pp. 18-26, 2004.
[21] N. Krahnstoever and R. Sharma, “Articulated Models from Video,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR '04), vol. 1, pp. 894-901, June 2004.
[22] G. Brostow, I. Essa, D. Steedly, and V. Kwatra, “Novel Skeletal Representation for Articulated Creatures,” Proc. Ninth European Conf. Computer Vision (ECCV '04), vol. 3, pp. 66-78, May 2004.
[23] N.I. Badler, C.B. Phillips, and B.L. Webber, Simulating Humans. Oxford Univ. Press, 1993.
[24] D. Gavrila and L. Davis, “3-D Model-Based Tracking of Humans in Action: A Multiview Approach,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR '96), pp. 73-80, 1996.
[25] M. Belkin and P. Niyogi, “Laplacian Eigenmaps for Dimensionality Reduction and Data Representation,” Neural Computation, vol. 15, no. 6, pp. 1373-1396, 2003.
[26] X. He, S. Yan, Y. Hu, P. Niyogi, and H.-J. Zhang, “Face Recognition Using Laplacianfaces,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 328-340, Mar. 2005.
[27] B. Schölkopf, A. Smola, and K.-R. Müller, “Nonlinear Component Analysis as a Kernel Eigenvalue Problem,” Neural Computation, vol. 10, pp. 1299-1319, 1998.
[28] S.T. Roweis and L.K. Saul, “Nonlinear Dimensionality Reduction by Locally Linear Embedding,” Science, vol. 290, no. 5500, pp.2323-2326, 2000.
[29] B. Nadler, S. Lafon, R. Coifman, and I. Kevrekidis, “Diffusion Maps, Spectral Clustering and the Eigenfunctions of Fokker-Planck Operators,” Proc. Conf. Neural Information Processing Systems (NIPS '05), Dec. 2005.
[30] T. Cox and M. Cox, Multidimensional Scaling. Chapman and Hall, 1994.
[31] F.R.K. Chung, Spectral Graph Theory. American Mathematical Society, 1997.
[32] B. Mohar, “The Laplacian Spectrum of Graphs,” Graph Theory, Combinatorics, and Applications, vol. 2, pp. 871-898, 1991.
[33] A. Sundaresan and R. Chellappa, “Acquisition of Articulated Human Body Models Using Multiple Cameras,” Proc. Fourth Conf. Articulated Motion and Deformable Objects, pp. 78-89, July 2006.
[34] A. Ozaslan, H.T.M. Yasar Iscan, I. Oxaslan, and S. Koc, “Estimation of Stature from Body Parts,” Forensic Science Int'l, vol. 132, no. 1, pp. 40-45, 2003.
[35] B.Y. Choi, Y.M. Chae, I.H. Chung, and H.S. Kang, “Correlation between the Postmortem Stature and the Dried Limb-Bone Lengths of Korean Adult Males,” Yonsei Medical J., vol. 38, no. 2, pp. 79-85, 1997.
[36] M.C.D. Mendonca, “Estimation of Height from the Length of Long Bones in a Portuguese Adult Population,” Am. J. Physical Anthropology, vol. 112, pp. 39-48, 2000.
6 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool