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.
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)
- 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)
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
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,
- .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.
Full Article in PDF
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.
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.
(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,
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
- ``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 -
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)