Image analysis – Applications – Target tracking or detecting
Reexamination Certificate
2007-05-15
2007-05-15
Ahmed, Samir (Department: 2624)
Image analysis
Applications
Target tracking or detecting
C382S190000, C382S191000
Reexamination Certificate
active
10654835
ABSTRACT:
A method detects objects in a scene over time. Sets of time-aligned features are extracted from multiple signals representing a scene over time; each signal is acquired using a different modality. Each set of time-aligned features is arranged as a vector in a matrix to which a first transform is applied to produce a compressed matrix. A second transform is applied to the compressed matrix to extract spatio-temporal profiles of objects occurring in the scene.
REFERENCES:
patent: 5995150 (1999-11-01), Hsieh et al.
patent: 6185309 (2001-02-01), Attias
patent: 6614428 (2003-09-01), Lengyel
Attias et al., “Blind source separation and deconvolution: the dynamic component analysis algorithm,” Neural Computation, 10: 1373-1424, 1998.
Barlow, H.B. (1989) Unsupervised learning. InNeural Computation1 pp. 295 311. MIT Press, Cambridge MA.
Hershey et al., in “Using audio-visual synchrony to locate sounds,” Advances in Neural Information Processing Systems 12. MIT Press, Cambridge MA 1999.
Bell, A. J. and Sejnowski, T. J. (1997). The independent components of natural scenes are edge filters. InVision Research, 37 (23) pp. 3327-3338.
Amari S-I., A. Cichocki and H. H. Yang (2000) . A New Learning Algorithm for Blind Signal Separation.
Smaragdis, Paris. (2001) Redundancy Reduction for Computational Audition, a Unifying Approach.Doctorate Thesis. Massachusetts Institute of Technology.
Slaney et al., in “Facesync: A linear operator for measuring synchronization of video facial images and audio tracks,” Advances in Neural Information Processing Systems 13, MIT Press, Cambridge MA, 2000.
Fisher et al., “Learning joint statistical models for audio-visual fusion and segregation,” Advances in Neural Information ProcessingSystems 13. MIT Press, Cambridge MA, 2001.
Casey, M., and Westner,W., (2000) Separation of Mixed Audio Sources by Independent Subspace Analysis, inProceedingsof the International Computer Music Conference, Berlin Aug. 2000.
Casey, M. (2001) . Reduced-Rank Spectra and Minimum Entropy Priors for Generalized Sound Recognition. In Proceedings of the Workshop on Consistent and ReliableCues for Sound Analysis, EUROSPEECH 2001, Aalborg, Denmark.
Hyv arinen, A. (1999) Survey on independent component analysis. InNeural Computing Surveys, 2, pp. 94-128.
M.G. and R.A. Calvo. (1998) Fast dimensionality reduction and simple PCA. InIntelligent Data Analysis, 2 (3) .
Roweis, S. (1997) EM Algorithms for PCA and SPCA. In M. I. Jordan,M. Kearns andS. Solla (eds.) ,Neural Information Processing Systems 10. MIT Press, Cambridge MA.
Casey Michael A.
Smaragdis Paris
Ahmed Samir
Brinkman Dirk
Mitsubishi Electric Research Laboratories Inc.
Mueller Clifton D.
Tabatabai Abolfazi
LandOfFree
Detecting temporally related components of multi-modal signals does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Detecting temporally related components of multi-modal signals, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Detecting temporally related components of multi-modal signals will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3749283