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M. Alex O. Vasilescu  
 


 
 
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   Multilinear ICA

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   TensorFaces

   Human Motion Signatures

   
Adaptive Meshes
 
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Multilinear Independent Component Analysis

Proprietary Position
US Patent Application No. 60/ filed Jan 13, 2004.

  • "Multilinear Independent Component Analysis", M. A. O. Vasilescu and D. Terzopoulos, Learning 2004 Snowbird, UT, April, 2004.

TensorTextures

Proprietary Position
US Patent Application No. 60/490,131 filed July 25, 2002.

  • "TensorTextures: Multilinear Image-Based Rendering", M. A. O. Vasilescu and D. Terzopoulos, Proc. ACM SIGGRAPH 2004 Conference Los Angeles, CA, August, 2004, in Computer Graphics Proceedings, Annual Conference Series, 2004, in press.
    Paper (5,104 KB - .pdf)

    Animations:
    • TensorTextures - AVI (54,225 KB), MOV (327,018 KB)
    • TensorTextures Strategic Dimensionality Reduction - AVI (19,650 KB), MOV (147,340 KB)

  • "TensorTextures", M. A. O. Vasilescu and D. Terzopoulos, Sketches and Applications SIGGRAPH 2003 San Diego, CA, July, 2003.
    Sketch (6MB - .pdf)


TensorFaces - Multilinear Tensor Decomposition of Image Ensembles

The goal of machine vision is automated image understanding and object recognition by a computer. Recent events have redoubled interest in biometrics and the application of computer vision technologies to non-obtrusive identification, surveillance, tracking, etc. Face recognition is a difficult problem for computers. This is due largely to the fact that images are the composite consequence of multiple factors relating to scene structure (i.e., the location and shapes of visible objects), illumination (i.e., the location and types of light sources), and imaging (i.e., viewpoint, viewing direction and camera characteristics). Multiple factors can confuse and mislead an automated recognition system. In addressing this problem, we take advantage of the assets of multilinear algebra, the algebra of higher-order tensors, to obtain a parsimonious representation that separates the various constituent factors. Our new representation of facial images, called TensorFaces, leads to improved recognition algorithms for use in the aforementioned applications.

Proprietary Position
US Patent Application No. 60/383,300 filed March 23, 2002.

  • ``Multilinear Subspace Analysis for Image Ensembles,'' M. A. O. Vasilescu, D. Terzopoulos, Proc. Computer Vision and Pattern Recognition Conf. (CVPR '03), Madison, WI, June, 2003, in press, 2003.
    Paper (1,657KB - .pdf)

  • ``Multilinear Image Analysis for Facial Recognition,'' M. A. O. Vasilescu, D. Terzopoulos, Proceedings of International Conference on Pattern Recognition (ICPR 2002), Quebec City, Canada, Aug, 2002.
    Paper (439KB - .pdf) , (3,427KB - .ps)

  • "Multilinear Analysis of Image Ensembles: TensorFaces," M. A. O. Vasilescu, D. Terzopoulos, Proc. 7th European Conference on Computer Vision (ECCV'02), Copenhagen, Denmark, May, 2002, in Computer Vision -- ECCV 2002, Lecture Notes in Computer Science, Vol. 2350, A. Heyden et al. (Eds.), Springer-Verlag, Berlin, 2002, 447-460.
    Abstract | Full Article in PDF (882KB)


Human Motion Signatures - 3-Mode Factor Analysis

Given motion capture samples of Charlie Chaplin's walk, is it possible to synthesize other motions---say, ascending or descending stairs---in his distinctive style? More generally, in analogy with handwritten signatures, do people have characteristic motion signatures that individualize their movements? If so, can these signatures be extracted from example motions? Furthermore, can extracted signatures be used to recognize, say, a particular individual's walk subsequent to observing examples of other movements produced by this individual?

We have developed an algorithm that extracts motion signatures and uses them in the animation of graphical characters. The mathematical basis of our algorithm is a statistical numerical technique known as n-mode analysis. For example, given a corpus of walking, stair ascending, and stair descending motion data collected over a group of subjects, plus a sample walking motion for a new subject, our algorithm can synthesize never before seen ascending and descending motions in the distinctive style of this new individual.

Proprietary Position
US Patent Application No. 60/337,912 filed December 6, 2001.

  • ``Human Motion Signatures: Analysis, Synthesis, Recognition,'' M. A. O. Vasilescu Proceedings of International Conference on Pattern Recognition (ICPR 2002) Quebec City, Canada, Aug, 2002.
    Paper (439KB - .pdf) , (3,427KB - .ps)

  • ``An Algorithm for Extracting Human Motion Signatures'', M. A. O. Vasilescu, Proceedings of Computer Vision and Pattern Recognition CVPR 2001 Lihue, HI, December, 2001.

  • "Human Motion Signatures for Character Animations", M. A. O. Vasilescu, Sketch and Applications SIGGRAPH 2001 Los Angeles, CA, August, 2001.
    Sketch (141KB - .pdf)

  • ``Recognition Action Events from Multiple View Points,'' Tanveer Sayed-Mahmood, Alex Vasilescu, Saratendu Sethi, in IEEE Workshop on Detection and Recognition of Events in Video, International Conference on Computer Vision (ICCV 2001), Vancuver , Canada, July 8, 2001.


Adaptive Meshes

Adaptive mesh models for the nonuniform sampling and reconstruction of visual data. Adaptive meshes are dynamic models assembled from nodal masses connected by adjustable springs. Acting as mobile sampling sites, the nodes observe interesting properties of the input data, such as intensities, depths, gradients, and curvatures. The springs automatically adjust their stiffnesses based on the locally sampled information in order to concentrate nodes near rapid variations in the input data. The representational power of an adaptive mesh is enhanced by its ability to optimally distribute the available degrees of freedom of the reconstructed model in accordance with the local complexity of the data.

We developed open adaptive mesh and closed adaptive shell surfaces based on triangular or rectangular elements. We propose techniques for hierarchically subdividing polygonal elements in adaptive meshes and shells. We also devise a discontinuity detection and preservation algorithm suitable for the model. Finally, motivated by (nonlinear, continuous dynamics, discrete observation) Kalman filtering theory, we generalize our model to the dynamic recursive estimation of nonrigidly moving surfaces.

  • ``Adaptive meshes and shells: Irregular triangulation, discontinuities, and hierarchical subdivision,'' M. Vasilescu, D. Terzopoulos, in Proc. Computer Vision and Pattern Recognition Conf. (CVPR '92), Champaign , IL, June, 1992, pages 829 - 832, 1992.
    Paper (652KB - .pdf)

  • ``Sampling and Reconstruction with Adaptive Meshes,'' D. Terzopoulos, M. Vasilescu, in Proc. Computer Vision and Pattern Recognition Conf. (CVPR '91), Lahaina, HI, June, 1991, pages 70 - 75, 1991.
    Paper (438KB - .pdf)