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Object detection in infrared images

  • David L. Milgram
  • Azriel Rosenfeld
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 109)

Abstract

This paper describes algorithms for detecting and classifying objects such as tanks and trucks in forward-looking infrared (FLIR) imagery. It summarizes research conducted in the course of a two-year project in the areas of image modeling, pre- and post-processing, segmentation, feature extraction, and classification.

Keywords

False Alarm Gray Level False Alarm Rate Object Region Border Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Algorithms and Hardware Technology for Image Recognition, First Quarterly Report, Computer Science Center, Univ. of Maryland, College Park, MD, July 1976.Google Scholar
  2. 2.
    Algorithms and Hardware Technology for Image Recognition, First Semiannual Report, Computer Science Center, Univ. of Maryland, College Park, MD, October 1976.Google Scholar
  3. 3.
    Algorithms and Hardware Technology for Image Recognition, Second Semiannual Report, Computer Science Center, Univ. of Maryland, College Park, MD, April 1977.Google Scholar
  4. 4.
    Algorithms and Hardware Technology for Image Recognition, Third Semiannual Report, Computer Science Center, Univ. of Maryland, College Park, MD, October 1977.Google Scholar
  5. 5.
    Panda, D. P., "Segmentation of FLIR Images by Pixel Classification", University of Maryland, Computer Science TR-508, Feb. 1977.Google Scholar
  6. 6.
    Panda, D. P., "Statistical Properties of Thresholded images", University of Maryland, Computer Science TR-559, July 1977.Google Scholar
  7. 7.
    Panda, D. P., "Statistical Analysis of Some Edge Operators", University of Maryland, Computer Science TR-558, July 1977.Google Scholar
  8. 8.
    Hueckel, M., "A Local Visual Operator Which Recognizes Edges and Lines", JACM, Vol. 20, 1973, pp. 634–647. [Erratum: JACM, Vol. 21, 1974, p. 350.]Google Scholar
  9. 9.
    Hueckel, M., "An Operator Which Locates Edges in Digitized Pictures", JACM, Vol. 18, 1971, pp. 113–125.Google Scholar
  10. 10.
    Hummel, R. A., "Edge Detection Using Basis Functions", University of Maryland, Computer Science TR-569, August 1977.Google Scholar
  11. 11.
    Mero, L., Vassy, Z., "A Simplified and Fast Version of the Hueckel Operator for Finding Optimal Edges in Pictures", Proc. 4th Intl. Conf. on Artif. Intelligence, Tbilisi, USSR, Sept. 1975, pp. 650–655.Google Scholar
  12. 12.
    Shaw, G. B., "Local and Regional Edge Detectors: Some Comparisons", University of Maryland, Computer Science TR-614, December 1977.Google Scholar
  13. 13.
    Peleg, S., "Iterative Histogram Modification, 2", University of Maryland, Computer Science TR-606, November 1977.Google Scholar
  14. 14.
    Davis, L. S., "A Survey of Edge Detection Techniques", Computer Graphics and Image Processing, Vol. 4, 1975, pp. 248–270.Google Scholar
  15. 15.
    Weszka, J. S., Rosenfeld, A., "Threshold Selection Using Weighted Histograms", University of Maryland, Computer Science TR-567, August 1977.Google Scholar
  16. 16.
    Milgram, D. L., Herman, M., "Clustering Edge Values for Threshold Selection", University of Maryland, Computer Science TR-617, December 1977.Google Scholar
  17. 17.
    Nakagawa, Y., Rosenfeld, A., "Some Experiments in Variable Thresholding", University of Maryland, Computer Science TR-626, January 1978.Google Scholar
  18. 18.
    Chow, C. K., Kaneko, T., "Automatic Boundary Detection of the Left Ventricle From Cineangiograms", Comput. Biomed. Res. 5, 1972, pp. 388–410.Google Scholar
  19. 19.
    Nakagawa, Y., Rosenfeld, A., "A Note on the Use of Local MIN and MAX Operations in Digital Picture Processing", University of Maryland, Computer Science TR-590, October 1977.Google Scholar
  20. 20.
    Milgram, D. L., "Constructing Trees for Region Description", University of Maryland, Computer Science TR-541, May 1977.Google Scholar
  21. 21.
    Rosenfeld, A., "Fuzzy Digital Topology", University of Maryland, Computer Science TR-573, September 1977.Google Scholar
  22. 22.
    Dyer, C. R., Rosenfeld, A., "Thinning Algorithms for Grayscale Pictures", University of Maryland, Computer Science TR-610, November 1977.Google Scholar
  23. 23.
    Ohlander, R., "Analysis of Natural Scenes", Ph.D. Thesis, Carnegie-Mellon University, Pittsburgh, PA, December 1976.Google Scholar
  24. 24.
    Milgram, D. L., Kahl, D. J., "Recursive Region Extraction", University of Maryland, Computer Science TR-620, December 1977.Google Scholar
  25. 25.
    Stockman, G. C., "Maryland Interactive Pattern Analysis and Classification System, Part I: Concepts", Dept. of Computer Science, University of Maryland TR-408, College Park, MD, September 1975.Google Scholar
  26. 26.
    Wertheimer, M., "Principles of Perceptual Organization", in Readings in Perception, D. C. Beardlee and M. Wertheimer (eds.), p. 122, Van Nostrand-Reinhold, Princeton, NJ, 1958.Google Scholar
  27. 27.
    Abend, K., "Compound Decision Procedures for Unknown Distributions and for Dependent States of Nature", Pattern Recognition, L. Kanal, Ed., Washington, DC, 1968, pp. 207–249.Google Scholar
  28. 28.
    Milgram, D. L., "Region Tracking Using Dynamic Programming", University of Maryland, Computer Science TR-539, May 1977.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1981

Authors and Affiliations

  • David L. Milgram
    • 1
  • Azriel Rosenfeld
    • 2
  1. 1.Lockheed Palo Alto Research Labs, D52–53, B204Palo Alto
  2. 2.Computer Vision Laboratory, Computer Science CenterUniversity of MarylandCollege Park

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