International Journal of Computer Vision

, Volume 12, Issue 1, pp 17–42 | Cite as

Toward color image segmentation in analog VLSI: Algorithm and hardware

  • Ffrank Perez
  • Christof Koch


Standard techniques for segmenting color images are based on finding normalized RGB discontinuities, color histogramming, or clustering techniques in RGB or CIE color spaces. The use of the psychophysical variable hue in HSI space has not been popular due to its numerical instability at low saturations. In this article, we propose the use of a simplified hue description suitable for implementation in analog VLSI. We demonstrate that if theintegrated white condition holds, hue is invariant to certain types of highlights, shading, and shadows. This is due to theadditive/shift invariance property, a property that other color variables lack. The more restrictive uniformly varying lighting model associated with themultiplicative/scale invariance property shared by both hue and normalized RGB allows invariance to transparencies, and to simple models of shading and shadows. Using binary hue discontinuities in conjunction with first-order type of surface interpolation, we demonstrate these invariant properties and compare them against the performance of RGB, normalized RGB, and CIE color spaces. We argue that working in HSI space offers an effective method for segmenting scenes in the presence of confounding cues due to shading, transparency, highlights, and shadows. Based on this work, we designed and fabricated for the first time an analog CMOS VLSI circuit with on-board phototransistor input that computes normalized color and hue.


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  1. Abdou, I.E., and Pratt, W.K. 1979. Quantitative design and evaluation of enhancement/thresholding edge detectors,Proc. IEEE 67(5): 753–763.Google Scholar
  2. Bajcsy, R., Lee, S.W., and Leonardis, A. 1990. Color image segmentation with detection of highlights and local illumination induced by inter-reflections.Proc. 10th Intern. Conf. Patt. Recog. B, Atlantic City, pp. 785–790.Google Scholar
  3. Barth, M., Parthasarathy, S., Wang, J., Hu, E., Hackwood, S., and Beni, G. 1986. A color vision system for microelectronics: Application to oxide thickness measurement,Proc. Inter. Conf. Robot. Autom., San Francisco, pp. 1242–1247.Google Scholar
  4. Berlin, B., and Kay, P. 1969.Basic Color Terms: Their Universality and Evolution, University of California.Google Scholar
  5. Canny, J. 1986. A computational approach to edge detection.IEEE Tran. Patt. Anal. Mach. Intell. 8(6): 679–698.Google Scholar
  6. Celenk, M. 1990. A color clustering technique for image segmentation,Comput. Vis., Graph. Image Process. 52: 145–170.Google Scholar
  7. Cook, R.L., and Torrance, K.E. 1981. A reflectance model for computer graphics,Computer Graphics 15(3): 307–316.Google Scholar
  8. Daily, M.J. 1989. Color image segmentation using Markov random fields,Proc. Conf. Comput. Vis. Patt. Recog., San Diego, pp. 304–312.Google Scholar
  9. Delbruck, T. 1993. Investigations of analog VLSI visual transduction and motion processing, Ph.D. Thesis, California Institute of Technology.Google Scholar
  10. Desimone, R., Schein, S.J., Moran, J., and Ungerleider, L.G. 1985. Contour, color and shape analysis beyond the striate cortex,Vision Research 25: 441.Google Scholar
  11. De Valois, R.L., and De Valois, K.K. 1975. Neural coding of color,Handbook of Perception, vol. VSeeing, Academic Press: San Diego, pp. 117–166.Google Scholar
  12. Dillon, P., Brault, A., Horak, J., Garcia, E., Martin, T., and Light, W. 1985. Fabrication and performance of color filter arrays for solid-state imagers,IEEE Trans. Electron Devices 25(2): 97–101.Google Scholar
  13. Foley, J.D., VanDam, A., Feiner, S.K., and Hughes, J.F. 1990.Computer Graphics: Principles and Practice, Addison-Wesley: Reading, MA.Google Scholar
  14. Geiger, D., and Girosi, F. 1990. Parallel and deterministic algorithms from MRFs: Surface reconstruction,IEEE Trans. Patt. Anal. Mach. Intell. 13(5): 401–412.Google Scholar
  15. Geman, S., and Geman, D. 1984. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,IEEE Trans. Patt. Anal. Mach. Intell. 5: 721–741.Google Scholar
  16. Genz, S.E. 1990. Real time chip set simplifies color image processing,Image Processing Handbook (8): 56–59, Data Translation™, Marlboro, MA.Google Scholar
  17. Gilbert, B. 1975. Translinear circuits: A proposed classification,Electronics Letters 11(1): 14–16.Google Scholar
  18. Gershon, R. 1985. Aspects of perception and computation in color vision,Comput. Vis. Graph. Image Process. 32: 245–278.Google Scholar
  19. Gershon, R., Jepson, A., and Tsotsos, J. 1986. Ambient illumination and the determination of material changes,J. Opt. Soc. Amer. A 3(10): 1700–1707.Google Scholar
  20. Harris, J.G., Koch, C., and Luo, J. 1990a. A two-dimensional analog VLSI circuit for detecting discontinuities in early vision,Science 248: 1209–1211.Google Scholar
  21. Harris, J.G., Koch, C., Staats, E., and Luo, J. 1990b. Analog hardware for detecting discontinuities in early vision,Intern. J. Comput. Vis. 4: 211–233.Google Scholar
  22. Healey, G. 1989. Using color for geometry-insensitive segmentation,J. Opt. Soc. Amer. A 6(6): 920–937.Google Scholar
  23. Healey, G., and Binford, T.O., 1987. The role and use of color in a general vision system.DARPA-Image Understanding Workshop, Los Angeles, pp. 599–613.Google Scholar
  24. Hurlbert, A., and Poggio, T. 1989. A network for image segmentation using color, InAdvances in Neural Information Processing Systems I, pp. 297–304, Morgan Kaufmann: San Mateo.Google Scholar
  25. Ingling, C.R., and Tsou, B.H. 1977. Orthogonal combination of the three visual channels,Vision Research 17: 1075–1082.Google Scholar
  26. Joblove, G.H., and Green, D. 1978. Color spaces for computer graphics,Computer Graphics 12(3): 20–25.Google Scholar
  27. Jain, A.K. 1989.Fundamentals of Digital Image Processing, Prentice-Hall: Englewood Cliffs, NJ.Google Scholar
  28. Kender, J. 1976. Saturation, hue, and normalized color: Calculation, digitization, and use, Computer Science Technical Report, Carnegie-Mellon University.Google Scholar
  29. Klinker, G.J., Shafer, S.A., and Kanade, T. 1988. The measurement of highlights in color images,Intern. J. Comput. Vis. 2: 7–32.Google Scholar
  30. Klinker, G.J., Shafer, S.A., and Kanade, T. 1990. A physical approach to color image understanding,Intern. J. Comput. Vis. 4: 7–38.Google Scholar
  31. Koch, C. 1989. Seeing chips: Analog VLSI circuits for computer vision,Neural Computation, 1: 184–200.Google Scholar
  32. Koch, C., Moore, A., Bair, W., Horiuchi, T., Bishofberger, B., and Lazzaro, J. 1991. Computing motion using analog VLSI vision chips: An experimental comparison among four approaches,IEEE Workshop on Visual Motion, Princeton, October, pp. 312–324.Google Scholar
  33. Lenny, P., and D'Zmura, M. 1988. Mechanisms of color vision,CRC Crit. Rev. Neurobiol. 3(4): 333–400.Google Scholar
  34. Mead, C. 1989.Analog VLSI and Neural Systems, Addison-Wesley: Reading, MA.Google Scholar
  35. Nevatia, R. 1977. A color edge detector and its use in scene segmentation,IEEE Trans. Syst. Man, Cyb. 7(11): 820–826.Google Scholar
  36. Ohlander, R.B. 1976. Analysis of Natural Scenes, Ph.D. thesis, Carnegie Mellon University.Google Scholar
  37. Ohta, Y., Kanade, T., and Sakai, T. 1980. Color information for region segmentation,Comput. Graph. Image Process. 13: 222–241.Google Scholar
  38. Perez, F.A., and Koch, C. 1992. Toward color image segmentation in analog VLSI,Rockwell 4th Annu. Tech. Conf. Cont. Sig. Process. pp. 246–263.Google Scholar
  39. Perez, F.A., and Koch, C. 1992. Segmenting color images using hue, CNS Memo 20, California Institute of Technology, 16 October.Google Scholar
  40. Phong, B.T. 1975. Illumination for computer generated pictures,Commu. ACM 18(6): 311–317.Google Scholar
  41. Poggio, T., Torre, V., and Koch, C. 1985. Computational vision and regularization theory,Nature 317(6035): 314–319.Google Scholar
  42. Poggio, T., Gamble, E.B., and Little, J.J. 1988. Parallel integration of vision modules,Science 242: 436–440.Google Scholar
  43. Rubin, J.M., and Richards, W.A. 1982. Color vision and image intensities: When are changes material?Biological Cybernetics 45: 215–226.Google Scholar
  44. Rubin, J.M., and Richards, W.A. 1984. Color vision: Representing material categories, AI Memo No. 764, Massachusetts Institute of Technology.Google Scholar
  45. Schwarz, M.W., Cowan, W.B., and Beatty, J.C. 1987. Experimental comparison of RGB, YIQ, LAB, and opponent color models,ACM Trans. Graphics 6(2): 123–158.Google Scholar
  46. Seevink, E. 1988. Analysis and synthesis of translinear integrated circuits, Elsevier: New York.Google Scholar
  47. Shafer, S.A. 1985. Using color to separate reflection components,COLOR Res. Appl. 10(4): 210–218.Google Scholar
  48. Sivilotti, M.A., Mahowald, and Mead, C.A. 1987. Real-time visual computation using analog CMOS processing arrays, 1987 Stanford Conf. VLSI, MIT Press: Cambridge, pp. 295–312.Google Scholar
  49. Smith, A.R. 1978. Color gamut transform pairs,Computer Graphics 12(3): 12–19.Google Scholar
  50. Terzopoulos, D. 1985. Computing visible-surface representations, AI Memo No. 800, Massachusetts Institute of Technology.Google Scholar
  51. Tominaga, S. 1987. Expansion of color images using three perceptual attributes,Patt. Recog. Lett. 6: 77–85.Google Scholar
  52. Tominaga, S. 1990. A color classification method for color images using a uniform color space,10th Intern. Conf. Patt. Recog., Atlantic City, pp. 803–807.Google Scholar
  53. Toumazou, C., Lidgey, F., and Haigh, D, eds. 1990.Analogue IC Design: The Current Mode Approach, Short Run Press, England.Google Scholar
  54. Wolfe, W.L., and Zissis, G.J. 1985.The Infrared Handbook, Environmental Research Institute of Michigan: Ann Arbor.Google Scholar
  55. Wright, W.A. 1989. A Markov random field approach to data fusion and colour segmentation,Image Vis. Comput. 7(2): 144–150.Google Scholar
  56. Wyszecki, G., and Stiles, W.S. 1982.Color Science: Concepts and Methods, Quantitative Data and Formulae, Wiley: New York.Google Scholar
  57. Zeki, S. 1983. Colour coding in the cerebral cortex: The reaction of cells in monkey visual cortex to wavelengths and colours,Neuroscience 9(4): 741–765.Google Scholar

Copyright information

© Kluwer Academic Publishers 1994

Authors and Affiliations

  • Ffrank Perez
    • 1
  • Christof Koch
    • 1
  1. 1.Computation and Neural Systems Program, 216-76California Institute of TechnologyPasadena

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