Image analysis – Applications – 3-d or stereo imaging analysis
Reexamination Certificate
2005-05-03
2005-05-03
Mehta, Bhavesh M. (Department: 2625)
Image analysis
Applications
3-d or stereo imaging analysis
C382S276000, C345S427000
Reexamination Certificate
active
06888960
ABSTRACT:
An efficient computation of low-dimensional linear subspaces that optimally contain the set of images that are generated by varying the illumination impinging on the surface of a three-dimensional object for many different relative positions of that object and the viewing camera. The matrix elements of the spatial covariance matrix for an object are calculated for an arbitrary pre-determined distribution of illumination conditions. The maximum complexity is reduced for the model by approximating any pair of normal-vector and albedo from the set of all such pairs of albedo and normals with the centers of the clusters that are the result of the vector quantization of this set. For an object, a viewpoint-independent covariance matrix whose complexity is large, but practical, is constructed and diagonalized off-line. A viewpoint-dependent covariance matrix is computed from the viewpoint-independent diagonalization results and is diagonalized online in real time.
REFERENCES:
patent: 5748844 (1998-05-01), Marks
patent: 5774576 (1998-06-01), Cox et al.
patent: 6121969 (2000-09-01), Jain et al.
patent: 6122408 (2000-09-01), Fang et al.
patent: 6169817 (2001-01-01), Parker et al.
patent: 6539126 (2003-03-01), Socolinsky et al.
Basri, R. et al., “Lambertian Reflectance and Linear Subspaces,”Int'l Conf. Comp. Vis., Vancouver, BC, to appear in Jul. 2001.
Everson, R.M. et al., “Inferring the Eigenvalues of Covariance Matrices From Limited, Noisy Data,”IEEE Trans. Sig. Proc., No. 48, vol. 7, pps. 2083-2091, 2000.
Georghiades, A.S. et al., “From Few to Many: Generative Models for Recognition Under Variable Pose and Illumination,”Proc. 4thInt'l Conf. Automatic Face&Gesture Recognition, pps. 264-270, Mar. 2000.
Hallinan, P., “A Low-Dimensional Representation of Human Faces for Arbitrary Lighting Conditions,”CVPR 94, pps. 995-999, 1994.
Ishiyama, R. et al., “A New Face-Recognition System with Robustness Against Illumination Changes,”IARP Workshop Mach. Vis. Appl., pps. 127-131, Nov. 2000.
Linde, Y. et al., “An Algorithm for Vector Quantizer Design,”IEEE Trans. Commun., No. 28, pps. 84-95, 1980.
Loeve, M. “Probability Theory,” pps. 455-493.
Penev, P.S., “Local Feature Analysis: A Statistical Theory for Information Representation and Transmission,” Ph.D. Thesis, The Rockefeller University, NY, NY, 1998.
Penev, P.S. et al., “The Global Dimensionality of Face Space,”Proc. 4thInt'l Conf. Automatic Face&Gesture Recognition, pps. 264-270, 2000.
Rose, K., “Deterministic Annealing for Clustering, Compression, Classification, Regression, and Related Optimization Problems,”Proc. IEEE, vol. 86, No. 11, pps. 2210-2239, 1998.
Sengupta, A.M. et al., “Distributions of Singular Values for Some Random Matrices,”Phys. Rev. E, vol. 60, No. 3, pps. 3389-3392, 1999.
Silverstein, J.W., “Eigenvalues and Eigenvectors of Large-Dimensional Sample Covariance Matrices,”Contemporary Mathematics, pps. 153-159, 1986.
Tipping, M.E. et al., “Mixtures of Probabilistic Principal Component Analysers,”Neural Comput., vol. 11, No. 2, pps 443-482, 1999.
Blicher Albert Peter
Ishiyama Rui
Penev Penio S.
Chawan Sheela
Mehta Bhavesh M.
NEC Corporation
Scully Scott Murphy & Presser
LandOfFree
Fast optimal linear approximation of the images of variably... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Fast optimal linear approximation of the images of variably..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Fast optimal linear approximation of the images of variably... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3446886