Image analysis – Learning systems – Neural networks
Reexamination Certificate
2005-07-12
2005-07-12
Mehta, Bhavesh M. (Department: 2625)
Image analysis
Learning systems
Neural networks
C382S118000, C382S224000, C382S276000
Reexamination Certificate
active
06917703
ABSTRACT:
The present invention may be embodied in a method, and in a related apparatus, for classifying a feature in an image frame. In the method, an original image frame having an array of pixels is transformed using Gabor-wavelet transformations to generate a transformed image frame. Each pixel of the transformed image is associated with a respective pixel of the original image frame and is represented by a predetermined number of wavelet component values. A pixel of the transformed image frame associated with the feature is selected for analysis. A neural network is provided that has an output and a predetermined number of inputs. Each input of the neural network is associated with a respective wavelet component value of the selected pixel. The neural network classifies the local feature based on the wavelet component values, and indicates a class of the feature at an output of the neural network.
REFERENCES:
patent: 4725824 (1988-02-01), Yoshioka
patent: 4805224 (1989-02-01), Koezuka et al.
patent: 4827413 (1989-05-01), Baldwin et al.
patent: 5159647 (1992-10-01), Burt
patent: 5168529 (1992-12-01), Peregrim et al.
patent: 5187574 (1993-02-01), Kosemura et al.
patent: 5220441 (1993-06-01), Gerstenberger
patent: 5280530 (1994-01-01), Trew et al.
patent: 5333165 (1994-07-01), Sun
patent: 5383013 (1995-01-01), Cox
patent: 5430809 (1995-07-01), Tomitaka
patent: 5432712 (1995-07-01), Chan
patent: 5465308 (1995-11-01), Hutcheson et al.
patent: 5511153 (1996-04-01), Azarbayejani et al.
patent: 5533177 (1996-07-01), Wirtz et al.
patent: 5550928 (1996-08-01), Lu et al.
patent: 5581625 (1996-12-01), Connell
patent: 5588033 (1996-12-01), Yeung
patent: 5680487 (1997-10-01), Markandey
patent: 5699449 (1997-12-01), Javidi
patent: 5703964 (1997-12-01), Menon et al.
patent: 5714997 (1998-02-01), Anderson
patent: 5715325 (1998-02-01), Bang et al.
patent: 5719954 (1998-02-01), Onda
patent: 5736982 (1998-04-01), Suzuki et al.
patent: 5764803 (1998-06-01), Jacquin et al.
patent: 5774591 (1998-06-01), Black et al.
patent: 5802220 (1998-09-01), Black et al.
patent: 5809171 (1998-09-01), Neff et al.
patent: 5828769 (1998-10-01), Burns
patent: 5842194 (1998-11-01), Arbuckle
patent: 5917937 (1999-06-01), Szeliski et al.
patent: 5982853 (1999-11-01), Liebermann
patent: 5995119 (1999-11-01), Cosatto et al.
patent: 6011562 (2000-01-01), Gagne
patent: 6044168 (2000-03-01), Tuceryan et al.
patent: 6052123 (2000-04-01), Lection et al.
patent: 6301370 (2001-10-01), Steffens et al.
patent: 4406020 (1995-06-01), None
patent: 0807902 (1997-11-01), None
patent: WO99/53443 (1999-10-01), None
A. J onathan Howell et al. “Towards unconstrained face recognition from image sequences”, IEEE Publication Date Oct. 14-16, 1996, pp. 224-229.
Notification of Transmittal of the International Report or the Declaration, International Search Report for PCT/US02/23973, mailed Nov. 18, 2002.
Valente, Stephanie et al., “A Visual Analysis/Synthesis Feedback Loop for Accurate Face Tracking”, Signal Processing Image Comunication, Elsevier Science Publishers, vol. 16, No. 6, Feb. 2001, pp. 585-608.
Yang, Tzong Jer, “Face Analysis and Synthesis”, Jun. 1, 1999, Retrieved from Internet, http://www.cmlab.csie.ntu.edu.tw/ on Oct. 25, 2002, 2 pg.
Yang, Tzong Jer, “VR-Face: An Operator Assisted Real-Tine Face Tracking System”, Communication and Multimedia Laboratory, Dept. of Computer Science and Information Engineering, National Taiwan University, Jun. 1999, pp. 1-6.
International Search Report for PCT/US99/07935.
Akimoto, T., et al., “Automatic Creation of Facial 3D Models”, IEEE Computer Graphics & Apps., pp. 16-22, Sep. 1993.
Ayache, N. et al., “Rectification of Images for Binocular and Trinocular Stereovision”, Pro. Of 9th Int'l., Conference on Pattern Recognition, 1, pp. 11-16, Italy, 1988.
Belhumeur, P., “A Bayesian Appraoch to Binocular Stereopsis”,Int'l. J. Of Computer Vision, 19 (3), pp. 237-260, 1996.
Beymer, D.J., “Face Recognition Under Varying Pose”, MIT A.I. Lab, Memo No. 1461, pp. 1-13, Dec. 1993.
Beymer, D.J., “Face Recognition Under Varying Pose”, MIT A.I. Lab. Research Report, 1994, pp. 756-761.
Buhmann, J. et al., “Distortion Invariant Object Recognition By Matching Hierarchically Labeled Graphs”, In Proceedings IJCNN Int'l Conf. Of Neural Networks, Washington, D.C. Jun. 1989, pp. 155-159.
DeCarlo, D., et al., “The Integration of Optical Flow and Deformable Models with Applications to Human Face Shape and Motion Estimation”, pp. 1-15, In Proc. CVPR '96, pp. 231-238 (published)[TM 18.9.96].
Devemay, F. et al., “Computing Differential Properties of 3-D Shapes from Steroscopic Images without {3-D} Models”, INRIA, RR-2304, pp. 1-28, Sophia, Antipolis, 1994.
Dhond, U., “Structure from Stereo: a Review”, IEEE Transactions on Systems, Man, and Cybernetics, 19(6), pp. 1489-1510, 1989.
Fleet, D.J., et al., “Computation of Component Image Velocity from Local Phase Information”,Int., J. Of Computer Vision, 5:1, pp. 77-104 (1990).
Fleet, D.J., et al.Measurement of Image Velocity, Kluwer Academic Press, Boston, pp. 1-203, 1992.
Hall, E.L., “Computer Image Processing And Recognition”, Academic Press 1979, 99, 468-484.
Hong, H.,et al., “Online Facial Recognition based on Personalized Gallery”, Proceedings of Int'l Conference On Automatic Face And Gesture Recognition, pp. 1-6, Japan Apr. 1997.
Kolocsai, P., et al, Statistical Analysis of Gabor-Filter Representation,Proceedings of International Conference on Automatic Face and Gesture Recognition, 1997, 4 pp.
Kruger, N., “Visual Learngng with a priori Constraints”,Shaker Verlag, Aachen, Germany, 1998, pp. 1-131.
Kruger, N., et al, “Principles of Cortical Processing Applied to and Motivated by Artificial Object Recognition”, Institut fur Neuroinformatik,Internal Report 97-17, Oct. 97, pp. 1-12.
Kruger, N., et al, “Autonomous Learning of Object Representations Utilizing Self-Controlled Movements”, 1998,Proceedings of NN98, 5 pp.
Kruger, N., et al, “Object Recognition with a Sparse and Autonomously Learned Representation Based on Banana Wavelets”,Internal Report 96-11, Institut fur Neuroinformatik, Dec. 96, pp. 1-24.
Kruger, N., et al, “Object Recognition with Banana Wavelets”,European Symposium on Artificial Neural Networks(ESANN97), 1997, 6 pp.
Kruger, N., “An Algorithm for the Learning of Weights in Discrimination Functions Usinga prioriConstrsints”,IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, No. 7, Jul. 1997, pp. 764-768.
Lades, M., et al, “Distortion Invarient Object Recognition in the Dynamic Link Architecture”,IEEE Transactions on Computers, vol. 42, No. 3, 1993, 11 pp.
Luong, Q. T., et al, “Fundamental Matrix, Theory, Algorithm, and Stability Analysis”,INRIA, 1993, pp. 1-46.
Manjunath, B. S., et al, “A Feature Based Approach to Face Recognition”,In Proceedings IEEE Conference on Computer Vision and Pattern Recognition, pp. 373-378, Mar. 1992.
Mauer, T., et al, “Single-View Based Recognition of Faces Rotated in Depth”, InProceedings of the International Workshop on Automatic Face and Gesture Recognition, pp. 248-253, Zurich, CH, Jun. 26, 1995.
Mauer, T., et al, “Learning Feature Transformations to Recognize Faces Rotated in Depth”, InProceedings of the International Conference on Artificial Neural Networks, vol. 1, pp. 353-358, Paris, France, Oct. 9-13, 1995.
Mauer, T., et al, “Tracking and Learning Graphs and Pose on Image Sequences of Faces”,Proceedings of 2nd International Conference on Automatic Face and Gesture Recognition, Oct. 14-16, 1996, pp. 176-181.
Maybank, S. J., et al, “A Theory of Self-Calibration of a Moving Camera”,International Journal of Computer Vision, 8(2), pp. 123-151, 1992.
McKenna, S.J., et al, Tracking Facial Feature Points With Gab
Adam Hartwig
Neven Hartmut
Steffens Johannes B.
Chawan Sheela C.
Fawcett Robroy R.
Nevengineering, Inc.
LandOfFree
Method and apparatus for image analysis of a gabor-wavelet... 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 apparatus for image analysis of a gabor-wavelet..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Method and apparatus for image analysis of a gabor-wavelet... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3371591