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Modeling Color Images for Machine Vision

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Image Technology

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Abstract

This chapter reviews and evaluates color image models that have been used in machine vision. Color image formation is described using models for image sensors, surfaces, and reflection processes. These models have been used to predict properties of color pixel distributions that will result for various classes of scenes. Several algorithms are described that use these distribution models for applications such as image segmentation, illuminant color estimation, and illumination invariant recognition. For textured images that are common in outdoor applications, benefits can be derived from using spatial interaction models for color images. A detailed summary is presented of recent work that introduces color texture models and applies these models to image segmentation and geometry invariant surface identification.

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References

  1. R. Bajcsy, S.W. Lee, and A. Leonardis. Color image segmentation with detection of highlights and local illumination induced by inter-reflections. In Proceedings of the International Conference on Pattern Recognition, pp. 785-790, Atlantic City, 1990.

    Google Scholar 

  2. R. Chellappa and A.K. Jain, editors. Markov Random Fields, theory and applications. Academic Press, San Diego, 1993.

    Google Scholar 

  3. J. Cohen. Dependency of the spectral reflectance curves of the munsell color chips. Psychonomic Sci., 1:369, 1964.

    Google Scholar 

  4. G.B. Coleman and H.C. Andrews. Image segmentation by clustering. Proceedings of the IEEE, 67(5):773–785, May 1979.

    Article  Google Scholar 

  5. M. Daily. Color image segmentation using markov random fields. IEEE Conference on Computer Vision and Pattern Recognition, pp. 304-312, 1989.

    Google Scholar 

  6. M. D’Zmura and P. Lennie. Mechanisms of color constancy. J. Opt. Soc. Am. A, 3(10):1662–1672, 1986.

    Article  Google Scholar 

  7. D. Forsyth. A novel algorithm for color constancy. International Journal of Computer Vision, 5(1):5–36, 1990.

    Article  MathSciNet  Google Scholar 

  8. R. Gershon, A. Jepson, and J. Tsotsos. Highlight identification using chromatic information. In Proceedings of the First International Conference on Computer Vision, pp. 161-170, London, June 1987.

    Google Scholar 

  9. R. Haralick and G. Kelly. Pattern recognition with measurement space and spatial clustering for multiple images. Proceedings IEEE, 57:654–665, April 1969.

    Article  Google Scholar 

  10. G. Healey. Using color for geometry insensitive segmentation. J. Opt. Soc. Am. A, 6:920–937, June 1989.

    Article  Google Scholar 

  11. G. Healey. Estimating spectral reflectance using highlights. Image and Vision Computing, 9(5):333–337, October 1991.

    Article  Google Scholar 

  12. G. Healey. Segmenting images using normalized color. IEEE Transactions on Systems, Man, and Cybernetics, 22(1):64–73, Jan/Feb 1992.

    Article  MathSciNet  Google Scholar 

  13. G. Healey and R. Kondepudy. Radiometric CCD camera calibration and noise estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994. Vol. 16, No. 3, March, 267–276.

    Article  Google Scholar 

  14. G. Healey, S. Shafer, and L. Wolff, editors. Physics-Based Vision: Principles and Practice, COLOR. Jones and Bartlett, Boston, 1992.

    Google Scholar 

  15. G. Healey and D. Slater. Global color constancy: recognition of objects by use of illumination — invariant properties of color distributions. J. Opt. Soc. Am. A, 11(11):3003–3010, November 1994.

    Article  Google Scholar 

  16. J. Ho, B.V. Funt, and M.S. Drew. Separating a color signal into illumination and surface reflectance components: Theory and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(10):966–997, October 1990.

    Article  Google Scholar 

  17. B.K.P. Horn and R. Sjoberg. Calculating the reflectance map. Applied Optics, 18:1770–1779, 1979.

    Article  Google Scholar 

  18. G.J. Klinker, S.A. Shafer, and T. Kanade. The measurement of highlights in color images. International Journal of Computer Vision, 2:7–32, 1988.

    Article  Google Scholar 

  19. G.J. Klinker, S.A. Shafer, and T. Kanade. A physical approach to color image understanding. International Journal of Computer Vision, 4:7–38, 1990.

    Article  Google Scholar 

  20. R. Kondepudy and G. Healey. Modeling and identifying 3-D color textures. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 577-582, New York City, 1993.

    Google Scholar 

  21. R. Kondepudy and G. Healey. Use of invariants for recognition of three-dimensional color textures. J. Opt. Soc. Am. A, 11(11):3037–3049, November 1994.

    Article  Google Scholar 

  22. H.C. Lee. Method for computing the scene-illuminant chromaticity from specular highlights. J. Opt. Soc. Am. A, 3(10):1694–1699, October 1986.

    Article  Google Scholar 

  23. H.C. Lee, E. Breneman, and C. Schulte. Modeling light reflection for color computer vision. IEEE Trans. on Pattern Analysis and Machine Intelligence, 12(4):402–409, April 1990.

    Article  Google Scholar 

  24. L. Maloney. Evaluation of linear models of surface spectral reflectance with small numbers of parameters. J. Opt. Soc. Am. A, 3(10):1673–1683, October 1986.

    Article  Google Scholar 

  25. L. Maloney and B. Wandell. Color constancy: A method for recovering surface spectral reflectance. J. Opt. Soc. Am. A, 3:1673–1683, October 1986.

    Article  Google Scholar 

  26. M. Nagao, T. Matsuyama, and Y. Ikeda. Region extraction and shape analysis in aerial photographs. Computer Graphics and Image Processing, 10:195–223, 1979.

    Article  Google Scholar 

  27. C. Novak and S. Shafer. Supervised color constancy for machine vision. In SPIE Proceedings Vol. 1453 on Human Vision, Visual Processing, and Digital Display II, pp. 353-368, 1991.

    Google Scholar 

  28. C. Novak and S. Shafer. Method for estimating scene parameters from color histograms. J. Opt. Soc. Am. A, 11(11):3020–3036, November 1994.

    Article  Google Scholar 

  29. C. Novak, S. Shafer, and R. Willson. Obtaining accurate color images for machine vision research. In SPIE Proceedings Vol. 1250 on Perceiving, Measuring, and Using Color, pp. 54-68, Santa Clara, 1990.

    Google Scholar 

  30. R. Ohlander, K. Price, and D.R. Reddy. Picture segmentation using a recursive region splitting method. Computer Graphics and Image Processing, 8:313–333, 1978.

    Article  Google Scholar 

  31. Y. Ohta, T. Kanade, and T. Sakai. Color information for region segmentation. Computer Graphics and Image Processing, 13:222–241, 1980.

    Article  Google Scholar 

  32. D. Panjwani and G. Healey. Segmentation of Textured Color Images. Technical report, University of California, Irvine, 1993.

    Google Scholar 

  33. D. Panjwani and G. Healey. Results using random field models for the segmentation of color images of natural scenes. In Proceedings of Fifth International Conference on Computer Vision, 714-719, Cambridge, 1995.

    Google Scholar 

  34. J. Reichman. Determination of absorption and scattering coefficients for nonhomogeneous media. 1: Theory. Applied Optics, 12(8):1811–1815, August 1973.

    Article  Google Scholar 

  35. J. Reichman. Determination of absorption and scattering coefficients for nonhomogeneous media. 2: Experiment. Applied Optics, 12(8):1816–1823, August 1973.

    Article  Google Scholar 

  36. A. Rosenfeld and L. Davis. Image segmentation and image models. Proceedings of the IEEE, 67(5):253–261, May 1979.

    Article  Google Scholar 

  37. A. Sarabi and J. Aggarwal. Segmentation of chromatic images. Pattern Recognition, 13(6):417–427, 1981.

    Article  Google Scholar 

  38. S. Shafer. Using color to separate reflection components. COLOR Research and Application, 10(4):210–218, Winter 1985.

    Article  Google Scholar 

  39. J. Silverman and D. Cooper. Bayesian clustering for unsupervised estimation of surface and texture models. IEEE Trans. on Pattern Analysis and Machine Intelligence, 10(4):482–495, July 1988.

    Article  MATH  Google Scholar 

  40. M. Swain and D. Ballard. Color indexing. Int. J. Comp. Vision, 7:11–32, 1991.

    Article  Google Scholar 

  41. G. Taubin and D. Cooper. Object recognition based on moment (or algebraic) invariants. In J. Mundy and A. Zisserman, editors, Geometric Invariance in Computer Vision, pp. 375–397. MIT Press, Cambridge, Mass, 1992.

    Google Scholar 

  42. S. Tominaga and B. Wandell. Standard surface-reflectance model and illuminant estimation. J. Opt. Soc. Am. A, 6(4):576–584, April 1989.

    Article  Google Scholar 

  43. S. Tominaga and B. Wandell. Component estimation of surface spectral reflectance. J. Opt. Soc. Am. A, 7(2):312–317, 1990.

    Article  Google Scholar 

  44. K. Torrance and E. Sparrow. Theory for off-specular reflection from roughened surfaces. J. Opt. Soc. Am., 57:1105–1114, 1967.

    Article  Google Scholar 

  45. W. Wright. A markov random field approach to data fusion and color segmentation. Image and Vision Computing, 7:144–150, 1989.

    Article  Google Scholar 

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Authors
  1. G. Healey
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Editors and Affiliations

  1. Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA

    Jorge L. C. Sanz

  2. IBM Argentina, Ingeniero Enrique Butty 275, 1300, Buenos Aires, Argentina

    Jorge L. C. Sanz

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© 1996 Springer-Verlag Berlin Heidelberg

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Healey, G. (1996). Modeling Color Images for Machine Vision. In: Sanz, J.L.C. (eds) Image Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-58288-2_4

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  • DOI: https://doi.org/10.1007/978-3-642-58288-2_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-63528-1

  • Online ISBN: 978-3-642-58288-2

  • eBook Packages: Springer Book Archive

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