Systems and methods for computer vision using curvelets

Image analysis – Pattern recognition – Template matching

Reexamination Certificate

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Reexamination Certificate

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08050503

ABSTRACT:
Certain embodiments of the present invention provide a system for computer vision including a plurality of images, a signature processor adapted to generate a signature based at least in part on a curvelet transform, and a matching processor adapted to receive a query image. The matching processor is adapted to determine a query signature for the query image using the signature processor. The matching processor is adapted to determine at least one matching image from the plurality of images based at least in part on the query signature.

REFERENCES:
patent: 6345274 (2002-02-01), Zhu et al.
patent: 6744935 (2004-06-01), Choi et al.
patent: 6754667 (2004-06-01), Kim et al.
patent: 6760714 (2004-07-01), Caid et al.
patent: 6834288 (2004-12-01), Chen et al.
patent: 6879394 (2005-04-01), Amblard et al.
patent: 7227893 (2007-06-01), Srinivasa et al.
patent: 7751621 (2010-07-01), Jacobsen
patent: 7805183 (2010-09-01), Keely et al.
patent: 2005/0286795 (2005-12-01), Zhang
patent: 2006/0029279 (2006-02-01), Donoho
patent: 2006/0147099 (2006-07-01), Marshall et al.
patent: 2007/0038691 (2007-02-01), Candes et al.
Irfan et al. (Automated Content based Image Retrieval using Wavelets), Transactions on engineering, computing and technology VI, Dec. 2004.
Wiley et al. (Using Quadratic Simplicial Elements for Hierarchical Approximation and Visualization), CIPIC, Feb. 2002.
Semler et al. (Curvelet-based Texture classification of Tissues in Computed Tomography), IEEE, 2006.
Dong et al. (Digital Curvelet Transform for Palmprint Recognition), Department of Automatic Control, Nationl University of Difense Technology, China, 2004.
Lei et al. (Image Curvelet Feature Extraction and Matching), Proc. ICIP, Oct. 1997.
Dong et al. (“Digital Curvelet Transform for Palmprint Recognition”, Sinobiometrics 2004, LNC 3338, p. 639-645, 2004).
B. Schiele and L. Crowley, Recognition without correspondence using multidimensional receptive field histograms, Comp. Vision 36 (2000), 31-50.
E. Simoncelli and W. Freeman, The steerable pyramid: A flexible architecture for multi-scale derivative computation, in Proc. IEEE ICIP, Washington, DC, 1995.
K. Tieu and P. Viola, Boosting image retrieval, Comp. Vision 56 (2004), 17-36.
G. Tzagkarakis, B. Beferull-Lozano and P. Tsakalides, Rotation-invariant texture retrieval with gaussianized steerable pyramids, IEEE Trans. Image Processing 15 (2006), 2702-2718.
USC-SIPI Image database (http://sipi.usc.edu/database/).
Z. Zhuang and M. Ouhyoung, Novel multiresolution metrics for content-based image retrieval, Proc. Fifth Pacific Conf. Computer Graphics and Applications 1997, 105-114.
C. Brambilla, A. Ventura, I. Gagliardi and R. Schettini: Multiresolution Wavelet Transform and Supervised Learning for Content-Based Image Retrieval, Proceedings of International Conference on Multimedia Communications Systems, vol. 1 (1999), 183-188.
E. J. Candès and D. L. Donoho, Continuous Curvelet Transform: I. Resolution of the Wavefront Set, Appl. Comput. Harmon. Anal. 19 (2005), 162-197.
E. J. Candès and D. L. Donoho, Continuous Curvelet Transform: II Discretization and Frames, Appl. Comput. Harmon. Anal. 19 (2005), 198-222.
E. Candès, L. Demanet, D. Donoho and L. Ying, Fast Discrete Curvelet Transforms, SIAM Multiscale Model. Simul. 5-3 (2006), 861-899.
M. Do, S. Ayer and M. Vetterli, Invariant image retrieval using the wavelet maxima moment, Proceedings of 3rd Int. Conf. on visual info. and info. systems, 1999.
P. Irfan, Sumari and K. Hailiza, Automated Content based Image Retrieval using Wavelets, Trans. Eng. Comp and Tech, Dec. 2004, 1305-5313.
C. Jacobs, A. Finkelstein and D. Salesin, Fast Multiresolution Image Querying, Proceedings of the 22nd annual conference on Computer graphics and interactive techniques 1995, 277-286.
M. Kobayakwa, M. Hoshi, T. Ohmori, Robust texture image retrieval using hierarchical correlations of wavelet coefficients, Proc. 15th International Conference on Pattern Recognition, vol. 3 (2000).
D. Po and M. Do, Directional multiscale modeling of images using the contourlet transform, IEEE Transactions on Image Processing 15 (2006), 1610-1620.
P. Burt and E. Adelson , The Laplacian pyramid as a compact image code, IEEE Trans. Communication 31 (1983), 532-540.
E. Candès and D. Donoho , New tight frames of Curvelets and optimal representations of objects with piecewise singularities, Comm. Pure App. Math. 57 (2003), 219-266.
Curvelets web-site (http://www.curvelets.org).
S. Dekel, D. Leviatan and M. Sharir, On bivariate smoothness spaces associated with nonlinear approximation, Constr. Approx. 20 (2004), 625-646.
M. Do and M. Vetterli, Rotation invariant texture characterization and retrieval using steerable wavelet-domain hidden Markov models, IEEE Trans. Multimedia 4 (2002), 517-527.
D.L. Donoho and A.G. Flesia, Can recent innovations in harmonic analysis ‘explain’ key findings in natural image statistics?, Network: Computation in Neural Systems 12 (2001), 371-393.
D. Field, Wavelets, vision and the statistics of natural scenes, Phil. Trans. R. Soc. Lond. A 357 (1999), 2527-2542.
W. Freeman and E. Adelson, The design and use of steerable filters, IEEE Trans. Patt. Anal. and Machine Intell. 13 (1991), 891-906.
H. Greenspan, S. Belongie, R. Goodman and P. Perona, Rotation invariant texture recognition using a steerable pyramid, ICPR 1994, Jerusalem, Israel, 162-167.
K. Guo, G. Kutyniok, and D. Labate, Sparse Multidimensional Representations using Anisotropic Dilation and Shear Operators, Wavelets and Splines (Athens, GA, 2005), Nashboro Press, Nashville, TN (2006), 189-201.
M. Hu, Visual pattern recognition by moment invariance, IRE Trans. Info. Theory 8 (1962), 179-187.
T. Kadir and M. Brady, Saliency, Scale and Image Description, Comp. Vision 45 (2001), 83-105.
N. Kingsbury, Complex wavelets for shift invariant analysis and filtering of signals, Appl. Comput. Harmon. Anal. 10 (2001), 234-253.
D. G. Lowe, Object Recognition from Local Scale-Invariant Features, Proc. IEEE Comp. Vision 2 (1999), 1150-1157.
B. Olshausen and D. Field, Neural Comput. 8 (2005), 1665-99.
S. Mallat, Wavelets for a vision, Proc. IEEE 84 (1998), 604-614.
S. Mallat and S. Zhong, Characterization of signals from multiscale edges, IEEE Trans. Pattern Anal. Machine Intelligence 14 (1992), 710-732.
K. Mikolajczyk, A. Zisserman and C. Schmid, Shape recognition with edge-based features, Proceedings of the British Machine Vision Conference (2003).
B. Schiele and L. Crowley, Recognition without correspondence using multidimensional receptive field histograms, Comp. Vision 36 (2000), 31-50.
E. Simoncelli and W. Freeman, The steerable pyramid: A flexible architecture for multi-scale derivative computation, in Proc. IEEE ICIP, Washington, DC, 1995.
K. Tieu and P. Viola, Boosting image retrieval, Comp. Vision 56 (2004), 17-36.
G. Tzagkarakis, B. Beferull-Lozano and P. Tsakalides, Rotation-invariant texture retrieval with gaussianized steerable pyramids, IEEE Trans. Image Processing 15 (2006), 2702-2718.
Z. Zhuang and M. Ouhyoung, Novel multiresolution metrics for content-based image retrieval, Proc. Fifth Pacific Conf. Computer Graphics and Applications 1997, 105-114.

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