Skip to main content
SpringerLink
Log in

Color to gray conversions in the context of stereo matching algorithms

An analysis and comparison of current methods and an ad-hoc theoretically-motivated technique for image matching

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

This study tackles the image color to gray conversion problem. The aim was to understand the conversion qualities that can improve the accuracy of results when the grayscale conversion is applied as a pre-processing step in the context of vision algorithms, and in particular dense stereo matching. We evaluated many different state of the art color to grayscale conversion algorithms. We also propose an ad-hoc adaptation of the most theoretically promising algorithm, which we call Multi-Image Decolorize (MID). This algorithm comes from an in-depth analysis of the existing conversion solutions and consists of a multi-image extension of the algorithm by Grundland and Dodgson (The decolorize algorithm for contrast enhancing, color to grayscale conversion, Tech. Rep. UCAM-CL-TR-649, University of Cambridge, 2005) which is based on predominant component analysis. In addition, two variants of this algorithm have been proposed and analyzed: one with standard unsharp masking and another with a chromatic weighted unsharp masking technique (Nowak and Baraniuk in IEEE Trans Image Process 7(7):1068–1074, 1998) which enhances the local contrast as shown in the approach by Smith et al. (Comput Graph Forum 27(2), 2008). We tested the relative performances of this conversion with respect to many other solutions, using the StereoMatcher test suite (Scharstein and Szeliski in Int J Comput Vis 47(1–3):7–42, 2002) with a variety of different datasets and different dense stereo matching algorithms. The results show that the overall performance of the proposed MID conversion are good and the reported tests provided useful information and insights on how to design color to gray conversion to improve matching performance. We also show some interesting secondary results such as the role of standard unsharp masking vs. chromatic unsharp masking in improving correspondence matching.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Alsam, A., Kolås, Ø.: Grey colour sharpening. In: Fourteenth Color Imaging Conference, pp. 263–267. Scottsdale, Arizona (2006)

  2. Badamchizadeh, M.A., Aghagolzadeh, A.: Comparative study of unsharp masking methods for image enhancement. In: International Conference on Image and Graphics, pp. 27–30 (2004)

  3. Bala, R., Eschbach, R.: Spatial color-to-grayscale transform preserving chrominance edge information. In: Color Imaging Conference, pp. 82–86 (2004)

  4. Berns R.S.: Billmeyer and Saltzman’s Principles of Color Technology, 3rd edn. Wiley-Interscience, New York (2000)

    Google Scholar 

  5. Birchfield S., Tomasi C.: Depth discontinuities by pixel-to-pixel stereo. Int. J. Comput. Vis. 35(3), 269–293 (1999)

    Article  Google Scholar 

  6. Black M., Rangarajan A.: On the unification of line processes, outlier rejection, and robust statistics with applications in early vision. Int. J. Comput. Vis. 19(1), 57–91 (1996)

    Article  Google Scholar 

  7. Bleyer, M., Chambon, S., Poppe, U., Gelautz, M.: Evaluation of different methods for using colour information in global stereo matching approaches. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXVII, part B3a, pp. 415–422 (2008)

  8. Čadík M.: Perceptual evaluation of color-to-grayscale image conversions. Comput. Graph. Forum 27(7), 1745–1754 (2008)

    Article  Google Scholar 

  9. Chambon, S., Crouzil, A.: Color stereo matching using correlation measures. In: Complex Systems Intelligence and Modern Technological Applications—CSIMTA 2004, Cherbourg, France, pp. 520–525. LUSAC (2004)

  10. Fairchild M., Pirrotta E.: Predicting the lightness of chromatic object colors using CIELAB. Color Res. Appl. 16(6), 385–393 (1991)

    Article  Google Scholar 

  11. Fairchild M.D.: Color Appearance Models, 2nd edn. Addison-Wesley, Boston (2005)

    Google Scholar 

  12. Gonzalez R.C., Woods R.E.: Digital Image Processing, 3rd edn. Prentice-Hall, Upper Saddle River (2006)

    Google Scholar 

  13. Gooch A.A., Olsen S.C., Tumblin J., Gooch B.: Color2gray: salience-preserving color removal. ACM Trans. Graph. 24(3), 634–639 (2005)

    Article  Google Scholar 

  14. Grundland, M., Dodgson, N.A.: The decolorize algorithm for contrast enhancing, color to grayscale conversion. Tech. Rep. UCAM-CL-TR-649, University of Cambridge, Computer Laboratory (2005)

  15. Grundland M., Dodgson N.A.: Decolorize: fast, contrast enhancing, color to grayscale conversion. Pattern Recogn. 40(11), 2891–2896 (2007)

    Article  Google Scholar 

  16. Guild J.: The colorimetric properties of the spectrum. Philos. Trans. R. Soc. Lond. A 230, 149–187 (1931)

    Article  Google Scholar 

  17. Hartley R.I., Zisserman A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004)

    Book  MATH  Google Scholar 

  18. Hirschmuller, H., Scharstein, D.: Evaluation of cost functions for stereo matching. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)

  19. Kolmogorov V., Zabih R.: Computing visual correspondence with occlusions via graph cuts. Tech. rep., Ithaca, NY, USA (2001)

    Google Scholar 

  20. Langford M.J.: Advanced Photography: A Grammar of Techniques. Focal Press, New York (1974)

    Google Scholar 

  21. Mantiuk R., Myszkowski K., Seidel H.P.: A perceptual framework for contrast processing of high dynamic range images. ACM Trans. Appl. Percept. 3(3), 286–308 (2006)

    Article  Google Scholar 

  22. Matthies L., Kanade T., Szeliski R.: Kalman filter-based algorithms for estimating depth from image sequences. Int. J. Comput. Vis. 3(3), 209–238 (1989)

    Article  Google Scholar 

  23. Nakamura, Y., Matsuura, T., Satoh, K., Ohta, Y.: Occlusion detectable stereo-occlusion patterns in camera matrix. In: CVPR ’96: Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR ’96), pp. 371–378. IEEE Computer Society, Washington (1996)

  24. Nayatani Y.: Simple estimation methods for the Helmholtz-Kohlrausch effect. Color Res. Appl. 22(6), 385–401 (1997)

    Article  Google Scholar 

  25. Nayatani Y.: Relations between the two kinds of representation methods in the Helmholtz-Kohlrausch effect. Color Res. Appl. 23(5), 288 (1998)

    Article  Google Scholar 

  26. Nayatani Y., Sakai H.: Confusion between observation and experiment in the Helmholtz-Kohlrausch effect. Color Res. Appl. 33(3), 250–253 (2008)

    Article  Google Scholar 

  27. Neumann, L., Čadík, M., Nemcsics, A.: An efficient perception-based adaptive color to gray transformation. In: Proceedings of Computational Aesthetics 2007, pp. 73–80. Eurographics Association, Banff, Canada (2007)

  28. Nowak R., Baraniuk R.: Adaptive weighted highpass filters using multiscale analysis. IEEE Trans. Image Process. 7(7), 1068–1074 (1998)

    Article  Google Scholar 

  29. de Queiroz R.L., Braun K.M.: Color to gray and back: color embedding into textured gray images. IEEE Trans. Image Process. 15(6), 1464–1470 (2006)

    Article  Google Scholar 

  30. Rasche K., Geist R., Westall J.: Detail preserving reproduction of color images for monochromats and dichromats. IEEE Comput. Graph. Appl. 25(3), 22–30 (2005)

    Article  Google Scholar 

  31. Reinhard E., Khan E.A., Akyz A.O., Johnson G.M.: Color Imaging: Fundamentals and Applications. A. K. Peters, Ltd., Natick (2008)

    Google Scholar 

  32. Scharstein, D., Pal, C.: Learning conditional random fields for stereo. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)

  33. Scharstein D., Szeliski R.: Stereo matching with nonlinear diffusion. Int. J. Comput. Vis. 28(2), 155–174 (1998)

    Article  Google Scholar 

  34. Scharstein D., Szeliski R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1–3), 7–42 (2002)

    Article  MATH  Google Scholar 

  35. Sharma G.: Digital Color Imaging Handbook. CRC Press, Boca Raton (2002)

    Book  Google Scholar 

  36. Shewchuk, J.R.: An introduction to the conjugate gradient method without the agonizing pain. Computer Science Tech. Report, pp. 94–125 (1994)

  37. Smith K., Landes P.E., Thollot J., Myszkowski K.: Apparent greyscale: a simple and fast conversion to perceptually accurate images and video. Computer Graphics Forum (Proceedings of Eurographics 2008) 27(2), 1745 (2008)

    Google Scholar 

  38. Tuytelaars T., Mikolajczyk K.: Local invariant feature detectors: a survey. Found. Trends Comput. Graph. Vis. 3(3), 177–280 (2008)

    Article  Google Scholar 

  39. Vergauwen M., Gool L.V.: Web-based 3D reconstruction service. Mach. Vis. Appl. 17(6), 411–426 (2006)

    Article  Google Scholar 

  40. Wright W.D.: A re-determination of the trichromatic coefficients of the spectral colors. Trans. Opt. Soc. 30, 141–164 (1928)

    Article  Google Scholar 

  41. Wyszecki G.: Correlate for lightness in terms of CIE chromaticity coordinates and luminous reflectance. J. Opt. Soc. Am. 57(2), 254–254 (1967)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Visual Computing Lab, Istituto di Scienza e Tecnologie della Informazione “A. Faedo”, Area della Ricerca CNR di Pisa, Via G. Moruzzi n. 1, 56124, Pisa, Italy

    Luca Benedetti, Massimiliano Corsini, Paolo Cignoni, Marco Callieri & Roberto Scopigno

Authors
  1. Luca Benedetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Massimiliano Corsini
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paolo Cignoni
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marco Callieri
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Roberto Scopigno
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Massimiliano Corsini.

Additional information

This work was funded by the EU IST IP 3DCOFORM.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Benedetti, L., Corsini, M., Cignoni, P. et al. Color to gray conversions in the context of stereo matching algorithms. Machine Vision and Applications 23, 327–348 (2012). https://doi.org/10.1007/s00138-010-0304-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-010-0304-x

Keywords

Search

Navigation