Data processing: database and file management or data structures – Database design – Database and data structure management
Reexamination Certificate
2011-03-29
2011-03-29
Vy, Hung T (Department: 2163)
Data processing: database and file management or data structures
Database design
Database and data structure management
C382S170000, C382S181000
Reexamination Certificate
active
07917540
ABSTRACT:
Variations in the states of patterns can be exploited for their discriminatory information and should not be discarded as noise. A pattern recognition system compares a data set of unlabeled patterns having variations of state in a set-by-set comparison with labeled arrays of individual data sets of multiple patterns also having variations of state. The individual data sets are each mapped to a point on a parameter space, and the points of each labeled array define a subset of the parameter space. If the point associated with the data set of unlabeled patterns satisfies a similarity criterion on the parameter space subset of a labeled array, the data set of unlabeled patterns is assigned to the class attributed to that labeled array.
REFERENCES:
patent: 4752957 (1988-06-01), Maeda
patent: 5223207 (1993-06-01), Gross et al.
patent: 5586066 (1996-12-01), White et al.
patent: 5629872 (1997-05-01), Gross et al.
patent: 5745382 (1998-04-01), Vilim et al.
patent: 5761090 (1998-06-01), Gross et al.
patent: 5764509 (1998-06-01), Gross et al.
patent: 5842194 (1998-11-01), Arbuckle
patent: 6119111 (2000-09-01), Gross et al.
patent: 6131076 (2000-10-01), Stephan et al.
patent: 6466685 (2002-10-01), Fukui et al.
patent: 6493465 (2002-12-01), Mori et al.
patent: 6996257 (2006-02-01), Wang
patent: 7224835 (2007-05-01), Maeda et al.
patent: 2005/0147291 (2005-07-01), Huang et al.
Vasconcelos, N. and Lippman, A., “A multiresolution manifold distance for invariant image similarity,” IEEE Transactions on Multimedia, 2005, 17 pages, University of California, posted at eScholarship Repository, <http://repositories.cdlib.org/postprints/692.
Arandjelovic, O. and Cipolla, R., “An information-theoretic approach to face recognition from face motion manifolds,” University of Cambridge, accepted Aug. 23, 2005, 9 pages, Copyright 2005, Elsevier B.V.
Gross, R., Matthews, I., and Baker, S., “Appearance-Based Face Recognition and Light-Fields,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, No. 4, Apr. 2004, published by IEEE Computer Society.
Fraser, A., Hengartner, N., Vixie, K. and Wohlberg, B., “Classification Modulo Invariance, with Application to Face Recognition,” 19 pages, Los Alamos National Laboratory.
Gross, R., Matthews, I., and Baker, S., “Eigen Light-Fields and Face Recognition Across Pose,” The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, 7 pages, appeared in 2002 International Conference on Automatic Face and Gesture Recognition.
Romdhani, Sr., Blanz, V., and Vetter, T., “Face Identification by Fitting a 3D Morphable Model using Linear Shape and Texture Error Functions,” University of Freiburg, Freiburg, Germany, appeared in European Conference on Computer Vision, 2002, Springer-Verlag, Germany.
Quan-Sen Sun, Pheng-Ann Heng, Zhong Jin, and De-Shen Xia, “Face Recognition Based on Generalized Canonical Correlation Analysis,” International Conference on Intelligent Computing 2005, Part II, Lecture Notes in Computer Sciences 3645, pp. 958-967, Copyright 2005 Springer-Verlag, Germany.
Shakhnarovich, G., Fisher, J.W., and Darrell, T., “Face recognition from long-term observations,” Artificial Intelligence Laboratory, Massachusetts Institute of Technology, white paper, 15 pages.
Arandjelovic, O., and Cipolla, R., “Face Recognition from Video using the Generic Shape-Illumination Manifold,” Department of Engineering, University of Cambridge, Cambridge, UK, white paper, 15 pages.
Fukui, K., and Yamaguchi, O., “Face Recognition Using Multi-viewpoint Patterns for Robot Vision,” Corporate Research and Development Center, Toshiba Corporation, Kawasaki, Japan, presented at 11th International Symposium of Robotics Research (ISRR) 2003, pp. 192-201.
Yamaguchi, O., Fukui, K., and Maeda, K., “Face Recognition Using Temporal Image Sequence,” Kansai Research Laboratories, Toshiba Corporation, Kobe, Japan, white paper, 6 pages.
Arandjelovic, O., Shakhnarovich, G., Fisher, J., Cipolla, R., and Darrell, T., “Face Recognition with Image Sets Using Manifold Density Divergence,” presented at Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Copyright 2005, Institute of Electrical and Electronics Engineers, Inc., 8 pages.
Nishiyama, M., Yamaguchi, O., and Fukui, K., “Face Recognition with the Multiple Constrained Mutual Subspace Method,” Audio- and Video-Based Biometric Person Authentication (AVBPA), 5th International Conference, Lecture Notes in Computer Science 3546, pp. 71-80, AVBPA 2005, Rye Brook, New York.
Gross, R., Matthews, I., and Baker, S., “Fisher Light-Fields for Face Recognition Across Pose and Illumination,” The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, 8 pages.
Georghiades, A.S., Belhumeur, P.N., and Kriegman, D.J., “From Few to Many: Generative Models for Recognition Under Variable Pose and Illumination,” Departments of Electrical Engineering and Computer Science, Yale University, New Haven, Connecticut, and Beckman Institute, University of Illinois Urbana Illinois, 8 pages.
Edelman, A., Arias, T.A., and Smith, S.T., “The Geometric of Algorithms With Orthogonality Constraints,” SIAM J. Matrix Anal. Appl., vol. 20, No. 2, pp. 303-353, Copyright 1998 Society for Industrial and Applied Mathematics.
Chang, Jen-Mei, Beveridge, J.R., Draper, B.A., Kirby, M., Kley, H., and Peterson, C., “Illumination Face Spaces are Idiosyncratic,” white paper, Colorado State University, Departments of Computer Science and Mathematics, Ft. Collins, Colorado, 7 pages.
Zhou, S.K. and Chellappa, R., “Image-based face recognition under illumination and pose variations,” Journal of the Optical Society of America A, vol. 22, No. 2, Feb. 2005, pp. 217-229, Copyright 2005 Optical Society of America.
Fitzgibbon, A.W. and Zisserman, A., “Joint Manifold Distance: a new approach to appearance based clustering,” white paper, University of Oxford, UK, Copyright 2003 Institute of Electrical and Electronics Engineers, Inc., 8 pages.
Basri, R., and Jacobs, D.W., “Lambertian Reflectance and Linear Subspaces,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, No. 2, Feb. 2003, pp. 218-233, published by IEEE Computer Society.
Tae-Kyun Kim, Kittler, Jr., and Cipolla, R., “Learning Discriminative Canonical Correlations for Object Recognition with Image Sets,” white paper, pp. 1-12, Dept. of Engineering, University of Cambridge, Cambridge, UK, Centre for Vision, Speech and Signal Processing, University of Surry, Guildford, UK.
Tae-Kyun Kim, Arandjelovic, O., and Cipolla, R., “Learing over Sets using Boosted Manifold Principal Angles (BoMPA),” Department of Engineering, University of Cambridge, Cambridge, UK, white paper, 10 pages.
Wolf, L. and Shashua, A., “Learning over Sets using Kernel Principal Angles,” Journal of Machine Learning Research 4, published Oct. 2003, pp. 913-931, Copyright Lior Wolf and Amnon Shashua.
Jen-Mei Change, Kirby, M., Kley, H., Peterson, C., Draper, B., and Beveridge, J.R., “Recogition of Digital Images of the Human Face at Ultra Low Resolution via Illumination Spaces,” white paper, Dept. of Mathematics, Colorado State University, Ft. Collins, Colorado, 11 pages.
Vasconcelos, N. and Lippman, A., “Multiresolution Tangent Distance for Affine-invarianct Classification,” white paper, 7 pages, MIT Media Laboratory, Cambridge, Massachusetts.
Liu, Xiuwen, Srivastava, A., and Gallivan, K., “Optimal Linear Representations of Images for Object Recognition,” presented at the Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 6 pages, Copyright 2003, Institute of Electrical and Electronics Engineers, Inc.
Conway, J.H., Hardin, R.H., and Sloane, N.J.A., “Packing Lines, Pl
Kirby Michael Joseph
Peterson Christopher Scott
Colorado State University Research Foundation
Hensley Kim & Holzer LLC
Vy Hung T
LandOfFree
Nonlinear set to set pattern recognition does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Nonlinear set to set pattern recognition, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Nonlinear set to set pattern recognition will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-2781798