Adaptive integrated image segmentation and object recognition

IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews

Volumn 30, Issue 4, 2000, Pages 427-441

  Bhanu, Bir ^a   Peng, Jing ^a  

^a University of California ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; AUTOMATA THEORY; COLOR IMAGE PROCESSING; IMAGE SEGMENTATION; RANDOM PROCESSES;

ADAPTIVE IMAGE SEGMENTATION; ADAPTIVE OBJECT RECOGNITION;

OBJECT RECOGNITION;

EID: 0034313634     PISSN: 10946977     EISSN: None     Source Type: Journal    
DOI: 10.1109/5326.897070     Document Type: Article

References (28)

1. W. Au and B. Roberts, "Overview of a self-adaptive ATR system via context-based configuration and control," in ARPA Image Understanding Workshop, Palm Springs, CA, Feb. 1996, pp. 585-588.
2. Y. Bengio and Y. Le Cun, "Word normalization for on-line handwritten word recognition," in Proc. Int. Conf. Pattern Recognition, Jerusalem, Israel, 1994, pp. 409-413.
3. B. Bhanu, S. Lee, and J. Ming, "Adaptive image segmentation using a genetic algorithm," IEEE Trans. Syst., Man, Cybern., vol. 25, pp. 1543-1567, Dec. 1995.
4. B. Bhanu, S. Lee, and S. Das, "Adaptive image segmentation using genetic and hybrid search methods," IEEE Trans. Aerosp. Electron. Syst., vol. 31, pp. 1268-1291, Oct. 1995.
5. B. Bhanu and S. Lee, Genetic Learning for Adaptive Image Segmentation. Norwell, MA: Kluwer, 1994.
6. B. Bhanu and T. L. Jones, "Image understanding research for automatic target recognition," IEEE Aerosp. Electron. Syst., vol. 8, pp. 15-23, Oct. 1993.
7. B. Bhanu and J. Ming, "Recognition of occluded objects: A cluster-structure algorithm," Pattern Recognit., vol. 20, no. 2, pp. 199-211, 1987.
8. C. Burges et al., "Shortest path segmentation: A method for training a neural network to recognize character strings," in Proc. Int. Joint Conf. Neural Networks, vol. 3, Baltimore, MD, 1992, pp. 165-172.
9. M. Dorigo and M. Colombetti, "Robot shaping: Developing autonomous agents through learning," Artif. Intell., vol. 71, pp. 321-370, Dec. 1994.
10. R. M. Haralick and L. G. Shapiro, "Image segmentation techniques," Comput. Vis., Graph., Image Process., vol. 29, pp. 100-132, 1985.
11. _, Computer and Robot Vision. Reading, MA: Addison-Wesley, 1992, vol. 1.
12. J. H. Holland, "Escaping brittleness: The possibilities of general-purpose learning algorithms applied to parallel rule-based systems," in Machine Learning: An Artificial Intelligence Approach, Vol. II, R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, Eds. San Mateo, CA: Morgan Kaufmann, 1986.
13. L. P. Kaelbling, M. L. Littman, and A. W. Moore, "Reinforcement learning: A survey," J. Artif. Intell. Res., vol. 4, pp. 237-285, 1996.
14. K. Laws, "The Phoenix image segmentation system: Description and evaluation,", SRI Int. Tech. Rep. TR289, Dec. 1982.
15. Y. LeCun, L. Bottou, and Y. Bengio, "Reading checks with graph transformer networks," in Proc. Int. Conf. Acoustics, Speech, Signal Processing, vol. 1, 1997, pp. 151-154.
16. R. Maclin and J. W. Shavlik, "Creating advice-taking reinforcement learners," Mach. Learn., vol. 22, no. 1-3, pp. 251-281, 1996.
17. T. Matsuyama, "Expert systems for image processing: Knowledge-based composition of image analysis processes," Comput. Vis., Graph., Image Process., vol. 48, pp. 22-49, 1989.
18. D. L. Milgram, "Region extraction using convergent series," Comput. Graph., Image Process., vol. 11, pp. 1-12, 1979.
19. R. Ohlander, K. Price, and D. R. Reddy, "Picture segmentation using a recursive region splitting method," Comput. Graph., Image Process., vol. 8, pp. 313-333, 1978.
20. J. Peng and B. Bhanu, "Closed-Loop object recognition using reinforcement learning," IEEE Trans. Pattern Anal. Machine Intell., vol. 20, no. 2, pp. 139-154, 1998.
21. _, "Delayed reinforcement learning for adaptive image segmentation and feature extraction," IEEE Trans. Syst., Man, Cybern. C, vol. 28, pp. 482-488, Aug. 1998.
22. J. Rasure and D. Argiro, Khoros User's Manual: Univ. New Mexico, Las Cruces, 1991.
23. A. Rosenfeld and A. C. Kak, Digital Picture Processing. New York: Academic, 1982.
24. S. Shafer and T. Kanade, "Recursive region segmentation by analysis of histograms," in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, 1982, pp. 1166-1171.
25. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press, 1998.
26. S. Thrun and A. Schwartz, "Finding structure in reinforcement learning," in Proc. Advances Neural Information Processing Systems 7, Denver, CO, 1994, pp. 385-392.
27. R. J. Williams, "Simple statistical gradient-following algorithms for connectionist reinforcement learning," Mach. Learn., vol. 8, pp. 229-256, 1992.
28. R. J. Williams and J. Peng, "Function optimization using connectionist reinforcement learning algorithms," Connect. Sci., vol. 3, no. 3, 1991.