Image analysis – Pattern recognition – Template matching
Reexamination Certificate
2011-06-07
2011-06-07
Chawan, Sheela C (Department: 2624)
Image analysis
Pattern recognition
Template matching
C382S168000, C382S190000, C382S305000, C707S999003, C707S999006, C707SE17025
Reexamination Certificate
active
07957596
ABSTRACT:
Computer-readable media, systems, and methods for flexible matching with combinational similarity are described. In embodiments, an object image is received, a query image is received, and the query image is compared with the object image. In various embodiments matching information is determined based upon combinational similarity and the matching information is presented to a user. In various embodiments, comparing the query image with the object image includes dividing the object image into agents, creating a gradient histogram for the agents, determining map areas for the query image, creating a gradient histogram for the map areas, and creating a similarity array for each of the agents. Further, in various embodiments, determining matching information includes creating a combinational array by combining the similarity arrays for each agent and determining whether the combinational array includes a peak value.
REFERENCES:
patent: 5943442 (1999-08-01), Tanaka et al.
patent: 6173066 (2001-01-01), Peurach et al.
patent: 6421463 (2002-07-01), Poggio et al.
patent: 6542621 (2003-04-01), Brill et al.
patent: 6741655 (2004-05-01), Chang et al.
patent: 6819797 (2004-11-01), Smith et al.
patent: 6882746 (2005-04-01), Naveen et al.
patent: 6904163 (2005-06-01), Fujimura et al.
patent: 6990233 (2006-01-01), Park et al.
patent: 7146028 (2006-12-01), Lestideau
patent: 2006/0239537 (2006-10-01), Shragai et al.
David G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” International Journal of Computer Vision, 2004, pp. 1-28.
Anuj Mohan, et al., “Example-Based Object Detection in Images by Components,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, No. 4, Apr. 2001, pp. 349-361, http://citeseer.ist.psu.edu/cache/papers/cs/22859/http:zSzzSzwww.ai.mit.eduzSzprojectszSzcbclzSzpublicationszSzpszSzmohan-ieee.pdf/mohan01examplebased.pdf.
Anonymous CVPR Submission, “Flexible Template Matching Algorithm,” CVPR 2007 Submission #2821, pp. 1-7.
Ondrej Chum, et al., “Geometric Hashing with Local Affine Frames,” CVPR 2006, 6 pages, Center for Machine Perception, Czech Technical University in Prague, Czech Republic.
Navnett Dalal, et al., “Histograms of Oriented Gradients for Human Detection,” Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 8 pages.
Fatih Porikli, “Integral Histogram: A Fast Way to Extract Histograms in Cartesian Spaces,” 8 pages, Mitsubishi Electric Research Laboratories, faith@merl.com, Dec. 2005.
Henning Muller, et al., “Logo and text removal for medical image retrieval,” 5 pages, http://www.dim.hcuge.ch/medgift/publications/bvm2005—mueller.pdf, University and University Hospitals of Geneva, 24, Rue Micheli-du-Crest, 1211 Geneva 14, Switzerland, henning.mueller@sim.hcuge.ch.
R. Fergus, et al., “Object Class Recognition by Unsupervised Scale-Invariant Learning,” 8 pages, Dept. of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, U.K., Jul 2003.
Henry Schneiderman, et al., “Object Detection Using the Statistics of Parts,” International Journal of Computer Vision 56(3), pp. 151-177, 2004 Kluwer Academic Publishers, Manufactured in The Netherlands, http://robotics.caltech.edu/readinggroup/vision/KanadeMultiscale.pdf.
Krystian Mikolajczyk, et al., “A performance evaluation of local descriptors,” pp. 1-34, Feb. 23, 2005.
Pedro F. Felzenszwalb, et al., “Pictorial Structures for Object Recognition,” pp. 1-42, Jan. 2005.
Yacov Hel-Or, et al., “Real-Time Pattern Matching Using Projection Kernels,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, No. 9, Sep. 2005, Published by the IEEE Computer Society.
S. Ravela, et al., “Retrieval of trademark and gray-scale images using global similarity,” pp. 1-12, http://ciir.cs.umass.edu/pubfiles/mm-25.pdf, 1998.
David Nister, et al., “Scalable Recognition with a Vocabulary Tree,” 8 pages, Dept. of Computer Science, University of Kentucky, 2006.
Eli Shechtman, et al., “Space-Time Behavior Based Correlation,” 8 pages, Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
Mishra Pragyana
Ofek Eyal
Wexler Yonatan
Chawan Sheela C
Microsoft Corporation
Shook Hardy & Bacon LLP
LandOfFree
Flexible matching with combinational similarity does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Flexible matching with combinational similarity, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Flexible matching with combinational similarity will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-2687516