Method and system for learning object recognition in images

Image analysis – Learning systems

Reexamination Certificate

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

C382S156000, C382S170000, C382S181000, C382S224000

Reexamination Certificate

active

07853071

ABSTRACT:
In a first exemplary embodiment of the present invention, an automated, computerized method for learning object recognition in an image is provided. According to a feature of the present invention, the method comprises the steps of providing a training set of standard images, calculating intrinsic images corresponding to the standard images and building a classifier as a function of the intrinsic images.

REFERENCES:
patent: 5787201 (1998-07-01), Nelson et al.
patent: 5872867 (1999-02-01), Bergen
patent: 6038337 (2000-03-01), Lawrence et al.
patent: 6594384 (2003-07-01), Kim et al.
patent: 7024033 (2006-04-01), Li et al.
patent: 7657089 (2010-02-01), Li et al.
patent: 2006/0177149 (2006-08-01), Friedhoff et al.
patent: 2007/0086649 (2007-04-01), Yang et al.
patent: 2007/0127816 (2007-06-01), Balslev et al.
Comaniciu, D., and Meer, P. “Mean Shift Analysis and Applications.”The Proceedings of the Seventh IEEE International Conference on Computer Vision.1999, vol. 2.
Finlayson, G. D., Hordley, S. D., Lu, C., and Drew, M. S. “On the Removal of Shadows From Images.”IEEE Transactions on Pattern Analysis and Machine Vision.2006, vol. 28, No. 1, pp. 59 to 68.
Freund, Y. and Schapire, R. “A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting.”Journal of Computer and System Sciences.Aug. 1997, vol. 55, No. 1, pp. 119 to 139.
Holub, A. and Perona, P. “A Discriminative Framework for Modelling Object Classes.”Proceedings of the 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR '05).Jun. 2005, vol. 1.
Khan, E. A., Fleming, R., and Buelthoff, H. “Image-Based Material Editing.” Association for Computing Machinery (ACM) Special Interest Group on Graphics and Interactive Techniques (SIGGRAPH) 2006 Papers, vol. 25, Issue 3.
Langer, M. S. and Zucker, S. W. “Shape-From-Shading on a Cloudy Day.”Journal of the Optical Society of America A.Feb. 1994, vol. 11, No. 2, pp. 467-478.
Nayar, S. K., Krishnan, G., Grossberg, M. D., and Raskar, R. “Fast Separation of Direct and Global Components of a Scene using High Frequency Illumination.” Association for Computing Machinery (ACM) Special Interest Group on Graphics and Interactive Techniques (SIGGRAPH) 2006 Papers, vol. 25, Issue 3.
Nayar, S. K., Nene, S. A., and Murase, H. “Real-Time 100 Object Recognition System.”Proceedings of the 1996 IEEE International Conference on Robotics and Automation.Apr. 1996, vol. 3., pp. 2321 to 2325.
Nishino, K. and Nayar, S. K. “Eyes for Relighting.”ACM Transactions on Graphics.Association for Computing Machinery (ACM) Special Interest Group on Graphics and Interactive Techniques (SIGGRAPH) 2004, vol. 23, Issue3.
Prados, E. and Faugeras, O. “Shape From Shading: a well-posed problem?”Proceedings of the 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR '05).Jun. 2005, vol. 2.
Rowley, H. A., Baluja, S., and Kanade, T. “Neural Network-Based Face Detection.”IEEE Transactions on Pattern Analysis and Machine Intelligence.Jan. 1998, vol. 20, No. 1.
Tappen, M. F., Adelson, E. H., and Freeman, W. T. “Estimating Intrinsic Component Images using Non-Linear Regression.”Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '06).Jun. 2006, vol. 2.
Tappen, M. F., Freeman, W. T., and Adelson, E. H. “Recovering Intrinsic Images from a Single Image.”IEEE Transanctions on Pattern Analysis and Machine Intelligence.Sep. 2005, vol. 27, No. 9.
Tu, Z. “Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering.”Tenth IEEE International Conference on Computer Vision,2005. Oct. 2005, vol. 2.
Viola, P. and Jones, M. “Rapid Object Detection using a Boosted Cascade of Simple Features.”Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.2001, vol. 1.
Worthington, P. L. and Hancock, E. R. “Object Recognition Using Shape-from Shading.”IEEE Transactions on Pattern Analysis and Machine Intelligence.May 2001, vol. 23, No. 5, pp. 535 to 542.
K. Barnard and G. Finlayson,Shadow Identification Using Colour Ratios,2002.
K. Barnard, G.D. Finlayson and B. Funt,Color Constancy for Scenes with Varying Illumination,Computer Vision and Image Understanding, 65(2): 311-321, Feb. 1997.
H.G. Barrow and J.M. Tenenbaum,Recovering Intrinsic Scene Characteristics from Imag,Computer Vision Systems, pp. 3-26, 1978.
C.F. Borges,A Trichromatic Approximation Method for Surface Illumination,Journal of Optical Society of America A, 8(8): 1319-1323., Aug. 1991.
M.S. Drew, G.D. Finlayson and S.D. Horley,Recovery of Chromaticity Image Free from Shadows via Illumination Invariance,Proceedings of IEEE Workshop on Color and Photometric Methods in Computer Vision, Nice, France 2003, pp. 32-39.
G.D. Finlayson, S.D. Horley and M.S. Drew,Removing Shadows from Images, 2002, pp. 2-14.
G. Finlayson,. “On the Removal of Shadows From Images,”IEEE Transactions on Pattern Analysis and Machine Intelligence.Jan. 2006, vol. 28, No. 1, pages 59 to 68.
G. Finlayson et al. “Intrinsic Images by Entropy Minimization,” May 2004: European Conference on Computer Vision, Prague, May 2004. Springer Lecture Notes in Computer Science, Vol. 3023, pp. 582 to 595.
G. Finlayson. et al. “Color constancy at a pixel,” J. Opt. Soc.Am. A. vol. 18, No. 2, Feb. 2001, pp. 253-264.
G.D. Funklea and R. Bajcsy,Combining Color and Geometry for the Active, Visual Recognition of Shadows,University of Pennsylvania Department of Computer & Information Science Technical Report No. MS-CIS-94-62, 1994.
R. Gershon, A.D. Jepson and J. K. Tsotsos,Ambient Illumination and the Determination of Material Changes,Journal of Optical Society of America A, 3(10):1700-1707, 1986.
J.M. Geusebroek, R.v.d. Bommgard and A.W.M. Smeulders,Color Invariance,IEEE Trans. On Pattern Analysis and Machine Intelligence, 23(12):1338-1350, Dec. 2001.
G.E. Healey,Using Color for Geometry-Insensitive Segmentation,Journal of Optical Society of America A, 6(6):920-937, Jun. 1989.
R. Hooke and T.A. Jeeves,Direct Search Solution of Numerical and Statistical Problems,Journal of the Association of Computing Machinery (JACM). Apr. 1961: vol. 8, Issue 2, pp. 212 to 229.
B.K.P. Horn,Determining Lightness from an Image,Computer Graphics and Image Processing, 3(1):277-299, Dec. 1974.
S. Kirkpatrick, C.D. Gelatt. and M.P. Vecchi,Optimization by Simulated Annealing,Science. May 13, 1983: vol. 220, No. 4598, pp. 671 to 680.
G.J. Klinker, S.A. Shafer and T. Kanade,A Physical Approach to Color Image Understanding,International Journal of Computer Vision, 4(1): 7-38, Jan. 1990.
E.H. Land and J.J. McCann,Lightness and Retinex Theory,Journal of Optical Society of America A, 61:1-11, 1971.
M.S. Langer,When Shadows Become Interreflections,International Journal of Computer Vision, 34(2/3), 193-204, 1999.
J.A. Marchant and C.M. Onyango,Shadow-Invariant Classification for Scenes Illuminated by Daylight,Journal of Optical Society of America A, 17(11), Nov. 2000.
S.K. Nayar, K. Ikeuchi and T. Kanade,Shape from Interreflections,IEEE International Conference onn cOmputr Vision (ICCV), pp. 2-11, Dec. 1990.
C. Reeves, ed.Modem Heuristic Techniques for Combinatorial Problems.New York: John Wiley and Sons, Inc., 1993 ISBN:0-470-22079-1.
I. Omer and M. Werman,Color Lines: Image Specific Color Representation,Proceeding of IEEE Conference on Computer Vision and Patter Recognition, pp. 946-953, Jun. 2004.
S.A Shafer,Using Color to Separate Reflection Components,Computer Science Department University of Rochester, TR 136, Apr. 1984.
S. Tominaga,Surface Identification Using Dichromatic Reflection Model,IEEE Transactions of Pattern Analysis and Machine Intelligence, 13(7), pp. 658-670, Jul. 1991.
S. Tominaga and N. Tanaka,Estimating Reflection Parameters from a Single Color Image,IEEE Comput. Graph. Appl., 20(5):58-66, 2000

LandOfFree

Say what you really think

Search LandOfFree.com for the USA inventors and patents. Rate them and share your experience with other people.

Rating

Method and system for learning object recognition in images does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Method and system for learning object recognition in images, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Method and system for learning object recognition in images will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFUS-PAI-O-4210088

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.