Object recognition using a cognitive swarm vision framework...

Data processing: artificial intelligence – Adaptive system

Reexamination Certificate

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C382S104000, C382S113000, C382S224000

Reexamination Certificate

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07599894

ABSTRACT:
An object recognition system is described that incorporates swarming classifiers with attention mechanisms. The object recognition system includes a cognitive map having a one-to-one relationship with an input image domain. The cognitive map records information that software agents utilize to focus a cooperative swarm's attention on regions likely to contain objects of interest. Multiple agents operate as a cooperative swarm to classify an object in the domain. Each agent is a classifier and is assigned a velocity vector to explore a solution space for object solutions. Each agent records its coordinates in multi-dimensional space that are an observed best solution that the agent has identified, and a global best solution that is used to store the best location among all agents. Each velocity vector thereafter changes to allow the swarm to concentrate on the vicinity of the object and classify the object when a classification level exceeds a preset threshold.

REFERENCES:
patent: 5911035 (1999-06-01), Tsao
patent: 2003/0142851 (2003-07-01), Brueckner et al.
Eberhart, Russel. Shi, Yuhui. “Particle Swarm Optimization: Developments, Applications, and Resources” IEEE, 2001, p. 81-86.
Bhanu, Blr. Lee, Sungkee. Ming, John. “Adaptive Image Segmentation Using a Genetic Algorithm.” IEEE Transactions on Systems, Man, and Cybernetics, vol. 25, No. 12, Dec. 1995. p. 1543-1567.
Doctor, Sheetal; Venayagamoorthy, Ganesh; Gudise, Venu; “Optimal PSO for Collective Robotic Search Applications”. IEEE 2004, p. 1390-1395.
Eberhart, Russel; Shi, Yuhui; “Guest Editorial Special Issue on Particle Swarm Optimization” IEEE Transactions on Evolutionary COmputation, vol. 8, No. 3 Jun. 2004. p. 201-203.
D.L. Swets, et al., “Genetic Algorithms for Object Recognition in a complex scene,” Proc. Of Intl. Conference on Image Processing, vol. 2, Oct., pp. 23-26, 1995.
V. Ciesielski, et al., “Using genetic algorithms to Improve the accuracy of object detection.” In Proceedings of the third Pacific-Asia Knowledge Discovery and Data Mining Conference. Ning Zhong and Lizhu Zhou (Eds.), Knowledge Discovery and Data Mining—Research and Practical Experiences. Tsinghua University Press, pp. 19-24, Beijing, China, Apr. 26-31, 1999.
Kennedy, J., et al., “Swarm intelligence,” San Francisco: Morgan Kaufmann Publishers, 2001.
R.C. Eberhart, et al., “Particle swarm optimization: Developments, applications, and resources,” 2001.
R. Brits, et al., “A Niching Particle Swarm Optimizer,” 2002.
Office action from U.S. Appl. No. 10/918,336; our ref. No. HRL155.
B. Bhanu, et al., “Adaptive Image Segmentation Using a Genetic Algorithm,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 25, No. 12, Dec. 1995.
S. Medasani and R. Krishnapuram, “Graph Matching by Relaxation of fuzzy assignments,” IEEE Transactions on Fuzzy Systems, 9(1), 173-183, Feb. 2001.
R. Krishnapuram, S. Medasani, S. Jung and Y. Choi, “Content-Based Image Retrieval Based on a Fuzzy Approach,” IEEE Transactions on Knowledge and Data Engineering (TKDE), Oct. 2004.
N. Oliver and A. Pentland, “Graphical models for driver behavior recognition in a smart car,” Proc. Of IV2000.
K. Sato and J.K. Aggarwal, “Temporal spatio-velocity transform and its application to tracking and interaction,” CVIU 96(2004), 100-128.
S. Hongeng, R. Nevatia, and F. Bremond, “Vide-based event recognition: activity representation and probabilistic recognition methods,” CVIU 96(2004), 129-162.
G. Medioni, I. Cohen, F. Bremond, S. Hongeng, R. Nevatia, “Event detection and analysis from video streams,” IEEE PAMI 23(8), 2001, 873-889.
N. Oliver, A. Garg, and E. Horvitz, “Layered representations for learning and inferring office activity from multiple sensory channels,” CVIU 96(2004), 163-180.
A. Amir, S. Basu, G. Iyengar, C. Lin, M. Naphade, J.R. Smith, S. Srinivasa, and B. Tseng, “A multi-modal system for retrieval of semantic video events,” CVIU 96(2004), 216-236.
R.T. Collins, A. J. Lipton, and T. Kanade, “Introduction to the special section on video surveillance,” IEEE-PAMI, 22(8), Aug. 2000.
N. Oliver, B. Rosario, and A. Pentland, “A Bayesian computer vision system for moceling human interactions,” IEEE-PAMI, 22(8), Aug. 2000.
Y. Owechko, S. Medasani, and N. Srinivasa, “Classifier Swarms for Human Detection in infrared imagery,” Proc. Of the CVPR workshop on Object Tracking and Classification Beyond the Visible Spectrum (OTCBVS'04) 2004.
M.P. Windham, “Numerical classification of proximity data with assignment measure,” Journal of Classification, vol. 2, pp. 157-172, 1985.
S. Gold and A. Rangarajan, “A graduated assignment algorithm for graph matching,” IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 18, pp. 377-387, Apr. 1996.
J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981.
Z. Zhang, “A flexible new technique for camera calibration,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11): 1330-1334, 2000.
Jean-Yves Bouguet, “Camera Calibration Toolbox for Matlab,” http://www.vision.caltech.edu/bouguetj/calib—doc/.
Intel OpenCV Computer Vision Library (C++), http://www.intel.com/research/mrl/research/opencv/.
Giorgio Carpaneto, Paolo Toth, “Algorithm 548: Solution of the assignment problem [H],” ACM Transactions on Mathematical Software, 6(1): 104-111, 1980.
I. Hartley, A. Zisserman, “Multiple view geometry in computer vision,” Cambridge University Press, Cambridge, UK 2000.
Longuet-Higgins, “A computer algorithm for reconstructing a scene from two projections” Nature, 293: 133-135, Sep. 1981.
T. Kailath, et al., “Linear Estimation,” Prentice Hall, NJ, ISBN 0-13-022464-2, 854pp, 2000.
P. Saisan, “Modeling Human Motion for recognition,” IS&T/SPIE 17th annual symposium, San Jose, CA 2005.
A.R. Dick, et al., “Combining Single view recognition and multiple view stereo for architectural scenes,” International Conference on Computer Vision (ICCV'01) vol. 1, Jul. 7-14, 2001, Vancouver, B.C., Canada.
G. Shakhanarovich, et al. “Integrated face and gait recognition from multiple views,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Dec. 2001, Kauai, Hawaii.
Sujit Kuthirummal, et al., “Planar shape recognition across multiple views,” In Proceedings of the Interationa Conference on Pattern Recognition (ICPR)—2002, Quebec, Canada.
Sujit Kuthirummal, et al., “Multiview constraints for recognition of planar curves in fourier domain,” Proceedings of the Indian Conference on Vision Graphics and Image Processing (ICVGIP)—2002.
A. Selinger and R.C. Nelson, “Appearance-based object recognition using multiple views,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition—Dec. 2001, Kauai, Hawaii.
F. Rojas, I. Rojas, R. M. Clemente, and C.G. Puntoner, “Nonlinear blind source separation using genetic algorithms,” in Proceedings of International Conference on Independent Component Analysis, 2001.
D. Beasley, D. R. Bull, and R. R. Martin, “A Sequential Niching Technique for Multimodal Function Optimization,” Evolutionary Computation, 1(2), p. 101-125, 1993.
R. Krishnapuram and J. M. Keller, “Quantative Analysis of Properties and Spatial Relations of Fuzzy Image Regions,” Transactions on Fuzzy Systems, 1(2):98-110, 1993.
Y. Owechko, et al., “Vision-Based occupant sensing and recognition for intelligent airbag systems,” submitted to IEEE Trans. On Intelligent Transportation Systems, 2003.
Y. Owechko and S. Medasani, “A Swarm-based Volition/Attention Framework for Object Recognition

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