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Color indexing

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Abstract

Computer vision is embracing a new research focus in which the aim is to develop visual skills for robots that allow them to interact with a dynamic, realistic environment. To achieve this aim, new kinds of vision algorithms need to be developed which run in real time and subserve the robot's goals. Two fundamental goals are determining the location of a known object. Color can be successfully used for both tasks.

This article demonstrates that color histograms of multicolored objects provide a robust, efficient cue for indexing into a large database of models. It shows that color histograms are stable object representations in the presence of occlusion and over change in view, and that they can differentiate among a large number of objects. For solving the identification problem, it introduces a technique calledHistogram Intersection, which matches model and image histograms and a fast incremental version of Histogram Intersection, which allows real-time indexing into a large database of stored models. For solving the location problem it introduces an algorithm calledHistogram Backprojection, which performs this task efficiently in crowded scenes.

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References

  • Aloimonos, J. 1990. “Purposive and qualitative active vision.”Proc. Int. Conf. Pat. Rec., pp. 346–360.

  • Aloimonos, J., Weiss, I., and Bandyopadhay, A. 1988 “Active vision.”Intern. J. Comput. Vision 1:436–440.

    Google Scholar 

  • Bajcsy, R. 1985. “Active perception vs. passive perception,”Workshop on Computer Vision: Representation and Control, pp. 55–59.

  • Bajcsy, R. 1988, “Active perception.”Proc. IEEE 76:996–1005.

    Google Scholar 

  • Ballard, D.H. 1987. “Interpolation coding: A representation for numbers in neural models.”Biological Cybernetics, 57:389–402.

    Google Scholar 

  • Ballard, D.H. 1989. “Reference frames for animate vision.”Intern. Joint Conf. Artif. Intell., pp. 1635–1641.

  • Ballard, D.H. 1991. “Animate vision.”Artificial Intelligence 48:57–86.

    Google Scholar 

  • Ballard, D.H., and Brown, C.M. 1982.Computer Vision. Prentice Hall: New York.

    Google Scholar 

  • Biederman, I. 1985. “Human image understanding: Recent research and a theory.”Comput. Vision, Graph. Image Process 32(1):29–73.

    Google Scholar 

  • Brainard, D.H., Wandell, B.A., and Cowan, W.B. 1989. “Black light: How sensors filter spectral variation of the illuminant.”IEEE Trans. Biomed. Engineer. 36:140–149.

    Google Scholar 

  • Chapman, D. 1990. “Vision, instruction, and action.” Technical Report 1204, Massachusetts Institute of Technology, Artificial Intelligence Laboratory, Cambridge, MA.

    Google Scholar 

  • Coombs, D.J. 1989. “Tracking objects with eye movements.”Proc. Topical Meet. Image Understand. Mach. Vision.

  • Dickmanns, E.D. 1988. “An integrated approach to feature based dynamic vision.”Proc. IEEE Conf. Comput. Vision and Patt. Recog., pp. 820–825.

  • Feldman, J.A. 1985. “Four frames suffice: A provisional model of vision and space.”Behav. Brain Sci. 8:265–289.

    Google Scholar 

  • Feldman, J.A., and Yakimovsky, Y. 1984. “Decision theory and artificial intelligence: I. A semantics-based region analyzer.”Artificial Intelligence 5:349–371.

    Google Scholar 

  • Forsyth, D.A. 1990. “A novel algorithm for color constancy.”Intern. J. Comput. Vision 5:5–35.

    Google Scholar 

  • Freeman, W.T., and Adelson, E.H. 1990. “Steerable filters for early vision, image analysis, and wavelet decomposition.”Proc. 3rd Intern. Conf. Comput. Vision, Osaka, pp. 406–415.

  • Garvey, T.D. 1986. “Perceptual strategies for purposive vision.” SRI International, Technical Note 117.

  • Hernstein, R.J. 1982. “Objects, categories, and discriminative stimuli.”Animal Cogn. Proc. Harry Frank Guggenheim Conf.

  • Klinker, G.J., Shafer, S.A., and Kanade, T. 1988. “The measurement of highlights in color images.”Intern. J. Comput. Vision, 2:7–32.

    Google Scholar 

  • Koenderink, J.J., and van, Doorn, A.J. 1976. “The singularities of the visual mapping.”Biological Cybernetics 24:51–59.

    Google Scholar 

  • Lennie, P., and D'Zmura, M. 1988. “Mechanisms of color vision.”CRC Crit. Rev. Neurobiol. 3:333–400.

    Google Scholar 

  • Malik, J., and Perona, P. 1990. “Preattentive texture discrimination with early vision mechanisms.”J. Opt. Soc. Amer. A. 7:923–932.

    Google Scholar 

  • Maloney, L.T., and Wandell, B. 1986. “Color constancy: A method for recovering surface spectral reflectance.”J. Opt. Soc. Amer. A 3(1):29–33.

    Google Scholar 

  • Maunsell, J.H.R., and Newsome, W.T. 1987. “Visual Processing in monkey extrastriate cortex.”Annu. Rev. Neurosci. 10:363–401.

    Google Scholar 

  • Mishkin, M., and Appenzeller, T. 1987. “The anatomy of memory.”Scientific American, June, pp. 80–89.

  • Nelson, R.C. 1989. “Obstacle avoidance using flow field divergence.”IEEE Trans. Patt. Anal. Mach. Intell. 11:1102–1106.

    Google Scholar 

  • Nelson, R.C. 1991. “Qualitative detection of motion by a moving observer.” In this issue.

  • Novak, C.L., and Shafer, S.A. 1990. “Supervised color constancy using a color chart.” School of Computer Science, Carnegie Mellon University, Technical Report CUM-CS-90-140.

  • Ohlander, R., Price, K., and Reddy, D.R. 1978. “Picture segmentation using a recursive region splitting method.”Comput. Graph. Image Process. 8:313–333.

    Google Scholar 

  • Olson, T.J., and Coombs, D.J. 1991. “Real-time vergence control for binocular robots.” In this issue.

  • Rubner, J., and Schulten, K. 1989. “A regularized approach to color constancy.”Biological Cybernetics 61:29–36.

    Google Scholar 

  • Strat, T.M. 1990, personal communication.

  • Swain, M.J. 1990a. “Color indexing.” Department of Computer Science, University of Rochester, Technical report 360.

  • Swain, M.J. 1990b. “Companion videotape to ‘color indexing’”.

  • Thompson, W.B. 1986. “Inexact vision.”Workshop on Motion, Representation, and Analysis, pp. 15–22.

  • Treisman, A. 1985. “Preattentive processing in vision.”Comput. Vision, Graph. Image Process. 31:156–177.

    Google Scholar 

  • Ullman, S. 1986. “An approach to object recognition: Aligning pictorial descriptions.” Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Technical Report 931.

  • Yarbus, A.L. 1967.Eye Movements and Vision. Plenum Press: New York.

    Google Scholar 

  • Young, T.Y., and Fu, K.S. eds. 1986.Handbook of Pattern Recognition and Image Processing. Academic Press: San Diego, CA.

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, University of Chicago, 60637, Chicago, IL

    Michael J. Swain

  2. Department of Computer Science, University of Rochester, 14627, Rochester, NY

    Dana H. Ballard

Authors
  1. Michael J. Swain
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  2. Dana H. Ballard
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Swain, M.J., Ballard, D.H. Color indexing. Int J Comput Vision 7, 11–32 (1991). https://doi.org/10.1007/BF00130487

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  • DOI: https://doi.org/10.1007/BF00130487

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