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ADORE: Adaptive Object Recognition

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Computer Vision Systems (ICVS 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1542))

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Abstract

Many modern computer vision systems are built by chaining together standard vision procedures, often in graphical programming environments such as Khoros, CVIPtools or IUE. Typically, these procedures are selected and sequenced by an ad-hoc combination of programmer’s intuition and trial-and-error. This paper presents a theoretically sound method for constructing object recognition strategies by casting object recognition as a Markov Decision Problem (MDP). The result is a system called ADORE (Adaptive Object Recognition) that automatically learns object recognition control policies from training data. Experimental results are presented in which ADORE is trained to recognize five types of houses in aerial images, and where its performance can be (and is) compared to optimal.

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References

  1. W. Au and B. Roberts. “Adaptive Configuration and Control in an ATR System,” IUW, pp. 667–676, 1996.

    Google Scholar 

  2. J. Aloimonos. “Purposive and Qualitative Active Vision”, IUW, pp 816–828, Sept. 1990.

    Google Scholar 

  3. K. Andress and A. Kak. “Evidence Accumulation and Flow of Control in a Hierarchical Spatial Reasoning System,” AI Magazine, 9(2):75–94, 1988.

    Google Scholar 

  4. M. Arbib. The Metaphorical Brain: An Introduction to Cybernetics as Artificial Intelligence and Brain Theory. Wiley Interscience, New York, 1972

    MATH  Google Scholar 

  5. D. Ballard. “Generalizing the Hough Transform to Detect Arbitrary Shapes,” PR, 13(2): 11–122, 1981.

    Google Scholar 

  6. R. Beveridge. LiME Users Guide. Technical report 97-22, Colorado State University Computer Science Department, 1997.

    Google Scholar 

  7. B. Burns, A. Hanson and E. Riseman. “Extracting Straight Lines,” PAMI 8(4):425–455, 1986.

    Google Scholar 

  8. S. Chien, H. Mortensen, C. Ying and S. Hsiao. “Integrated Planning for Automated Image Processing,” Integrated Planning Applications, AAAI Spring Symposium Series, March 1995, pp 26–35.

    Google Scholar 

  9. V. Clement and M. Thonnat. “A Knowledge-Based Approach to Integration of Image Processing Procedures,” CGVIP, 57(2):166–184, 1993.

    Google Scholar 

  10. D. Comaniciu and P. Meer. “Robust Analysis of Feature Space: Color Image Segmentation,” CVPR, pp. 750–755, 1997.

    Google Scholar 

  11. B. Draper, R. Collins, J. Brolio, A. Hanson and E. Riseman. “The Schema System,” IJCV, 2(2):209–250, 1989.

    Article  Google Scholar 

  12. B. Draper and A. Hanson. “An Example of Learning in Knowledge Directed Vision,” Theory and Applications of Image Analysis, World Scientific, Singapore, 1992. pp. 237–252.

    Google Scholar 

  13. B. Draper. “Modelling Object Recognition as a Markov Decision Process,” ICPR, D95–99, 1996.

    Google Scholar 

  14. B. Draper and K. Baek. “Bagging in Computer Vision,” CVPR, pp. 144–149, 1998.

    Google Scholar 

  15. V. Hwang, L. Davis and T. Matsuyama. “Hypothesis Integration in Image Understanding Systems,” CGVIP, 36(2):321–371, 1986.

    Google Scholar 

  16. K. Ikeuchi and M. Hebert. “Task Oriented Vision,” IUW, pp. 497–507, 1990.

    Google Scholar 

  17. X. Jiang and H. Bunke. “Vision planner for an intelligent multisensory vision system,” Automatic Object Recognition IV, pp. 226–237, 1994.

    Google Scholar 

  18. M. Kass, A. Witken and D. Terzopoulis. “Snakes: Active Contour Models,” IJCV 1(4):321–331, 1988.

    Article  Google Scholar 

  19. A. Lansky, et al. “The COLLAGE/KHOROS Link: Planning for Image Processing Tasks,” Integrated Planning Applications, AAAI Spring Symposium Series, 1995,pp 67–76.

    Google Scholar 

  20. M. Maloof, P. Langley, S. Sage, T. Binford. “Learning to Detect Rooftops in Aerial Images,” IUW, 835–846, 1997.

    Google Scholar 

  21. W. Mann and T. Binford. “SUCCESSOR: Interpretation Overview and Constraint System,” IUW, 1505–1518, 1996.

    Google Scholar 

  22. D. McKeown, W. Harvey and J. McDermott. “Rule-Based Interpretation of Aerial Imagery,” PAMI, 7(5);570–585, 1985.

    Google Scholar 

  23. J. Mundy. “The Image Understanding Environment Program,” IEEE Expert, 10(6):64–73, 1995.

    Article  Google Scholar 

  24. M. Nagao and T. Matsuyama. A Structural Analysis of Complex Aerial Photographs. N.Y.: Plenum Press, 1980.

    Google Scholar 

  25. D. Nguyen. An Iterative Technique for Target Detection and Segmentation in IR Imaging Systems, Technical Report, Center for Night Vision and Electro-Optics, 1990.

    Google Scholar 

  26. J. Peng and B. Bhanu. “Closed-Loop Object Recognition using Reinforcement Learning,” PAMI 20(2):139–154, 1998.

    Google Scholar 

  27. J. Rasure and S. Kubica. “The KHOROS Application Development Environment,” In Experimental Environments for Computer Vision, World Scientific, New Jersey, 1994.

    Google Scholar 

  28. S. Ravela, B. Draper, J. Lim and R. Weiss. “Tracking Object Motion Across Aspect Changes for Augmented Reality,” IUW, pp. 1345–1352, 1996.

    Google Scholar 

  29. R. Rimey and C. Brown. “Control of Selective Perception using Bayes Nets and Decision Theory,“ IJCV, 12(2): 173–207.

    Google Scholar 

  30. R. Sutton. “Learning to Predict by the Methods of Temporal Differences,” ML, 3(9):9–44, 1988.

    Google Scholar 

  31. G. Tesauro. “Temporal Difference Learning and TD-Gammon,” CACM, 38(3):58–68, 1995.

    Google Scholar 

  32. S. Ullman. “Visual Routines,” Cognition, 18:97–156, 1984.

    Article  Google Scholar 

  33. S. Umbaugh. Computer Vision and Image Processing: A Practical Approach using CVIPtools, Prentice Hall, New Jersey, 1998.

    Google Scholar 

  34. C. Watkins. Learning from Delayed rewards, Ph.D. thesis, Cambridge University, 1989.

    Google Scholar 

  35. W. Zhang and T. Dietterich. “A Reinforcement Learning Approach to Job-Shop Scheduling,” IJCAI, 1995.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, Colorado State University, Fort Collins, CO, USA, 80523

    Bruce A. Draper, Jose Bins & Kyungim Baek

Authors
  1. Bruce A. Draper
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  2. Jose Bins
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  3. Kyungim Baek
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© 1999 Springer-Verlag Berlin Heidelberg

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Draper, B.A., Bins, J., Baek, K. (1999). ADORE: Adaptive Object Recognition. In: Computer Vision Systems. ICVS 1999. Lecture Notes in Computer Science, vol 1542. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49256-9_31

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  • DOI: https://doi.org/10.1007/3-540-49256-9_31

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65459-9

  • Online ISBN: 978-3-540-49256-6

  • eBook Packages: Springer Book Archive

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