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Quan, Wei; Matuszewski, Bogdan; Shark, Lik; Ait-Boudaoud, Djamel (2007)
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Languages: English
Types: Part of book or chapter of book
Subjects:

Classified by OpenAIRE into

arxiv: Computer Science::Computer Vision and Pattern Recognition
ACM Ref: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, ComputingMethodologies_COMPUTERGRAPHICS
This paper describes initial results from a novel low dimensional surface parameterisation approach based on a modified iterative closest point (ICP) registration process which uses vertex based principal component analysis (PCA) to incorporate a deformable element into registration process. Using this method a 3D surface is represented by a shape space vector of much smaller dimensionality than the dimensionality of the original data space vector. The proposed method is tested on both simulated 3D faces with different facial expressions and real face data. It is shown that the proposed surface representation can be potentially used as feature space for a facial expression recognition system.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] 3dMD, www.3dMD.com. 3dMD 3D Scanner, 2006.
    • [2] K. S. Arun and T. S. Huang. Least-square tting of two 3-d point sets. IEEE TVGC., 9(5):698–700, 1987.
    • [3] A. T. Basilevsky. Statistical Factor Analysis and Related Methods: Theory and Applications. Wiley Interscience, 1994.
    • [4] S. Belongie, J. Malik, and J. Puzicha. Shape matching and object recognition using shape contexts. IEEE Trans. PAMI., 24(4):509–522, 2002.
    • [5] P. J. Besl and R. C. Jain. Invariant surface characteristics for 3d object recognition in range images. Comput. Vision Graph. Image Process, 33(1):33–80, 1986.
    • [6] P. J. Besl and N. D. McKay. A method for registration of 3-d shapes. IEEE Trans. PAMI., 14(2):239–256, 1992.
    • [7] A. Blake and M. Isard. Active Contours. Springer-Verlag Berlin and Heidelberg, 1998.
    • [8] H. Chui and A. Rangarajan. A new point matching algorithm for non-rigid registration. Comput. Vis. Image Underst., 89(2-3):114–141, 2003.
    • [9] T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham. Active shape models - their training and application. Comput. Vis. Image Underst., 61(1):38–59, 1995.
    • [10] A. E. Johnson and M. Hebert. Using spin images for ef cient object recognition in cluttered 3d scenes. IEEE Trans. PAMI, 21(5):433–449, 1999.
    • [11] H. Qin and D. Terzopoulos. D-nurbs: A physic-based framework for geometric design. IEEE TVGC., 2(1):85–96, 1996.
    • [12] D. F. Rogers. A Introduction to NURBS with Historical Perspective. Academic Press, 2001.
    • [13] Singular Inversions, www.facegen.com. FaceGen Modeller, 2003.
    • [14] S. Umeyama. Least-square estimation of transformation parameters between two point patterns. IEEE TVGC., 13(4):376–380, 1991.
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