DESIGN OF A CUSTOMIZED PATTERN FOR IMPROVING COLOR CONSTANCY ACROSS CAMERA AND ILLUMINATION CHANGES

Hazem Wannous, Sylvie Treuillet, Yves Lucas, Alamin Mansouri, Yvon Voisin

2010

Abstract

This paper adresses the problem of color constancy on a large image database acquired with varying digital cameras and lighting conditions. Automatic white balance control proposed by an available commercial camera is not sufficient to provide reproducible color classification. A device-independent color representation may be obtained by applying a chromatic adaptation transform, from a calibrated color checker pattern included in the field of view. Instead of using the standard Macbeth color checker, we suggest to select judicious colors to design a customized pattern from contextual information. A comparative study demonstrates that this approach insures a stronger constancy of the interesting colors before the vision control.

References

  1. Barnard, K. and Funt, B. (2002). Camera characterization for color research. Color Research and Application, 27(3):153-164.
  2. Barnard, K., Martin, L., Coath, A., and Funt, B. (2002). A comparison of computational colour constancy algorithms, part ii : Experiments with image data. IEEE Trans. on Image Processing, 11(9):985-999.
  3. Cheng, S. and Yang, C. (2001). A fast and novel technique for color quantization using reduction of color space dimensionality. Pattern Recognition Letters, 22 (8):845-856.
  4. CIE (2008). Colorimetry part 2: Standard illuminants for colorimetry. Technical report, Joint ISO/CIE Standard ISO 11664-2:2008(E)/CIE S 014-2/E:2006, CIE Central Bureau, Kegelgasse 27, A-1030 Vienna, Austria.
  5. Debevec, P. E. and Jitendra, M. (1997). Recovering high dynamic range radiance maps from photographs. In Proc. of the 24th annual conference on Computer graphics and interactive techniques, pages 369-378.
  6. Deng, Y., Kenney, S., Moore, M., and Manjunath, B. S. (1999). Peer group filtering and perceptual color image quantization. In IEEE Inter. Symp. on Circ. and Sys. VLSI (ISCAS'99), volume 4, pages 21-24, Orlando, FL.
  7. Deng, Y. and Manjunath, B. S. (2001). Unsupervised segmentation of colour-texture regions in images and video. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI 7801), 23:800-810.
  8. Grossberg, M. and Nayar, S. (2002). What can be known about the radiometric response function from images ? In Lecture Notes in Computer Sciences (ECCV), volume 2353, pages 393-413.
  9. Haeghen, Y. V., Naeyaert, J. M., Lemahieu, I., and Philips, W. (2000). An imaging system with calibrated color image acquisition for use in dermatology. IEEE Trans. on Medical Imaging, 19(7):722-730.
  10. Hsieh, I.-S. and Fan, K.-C. (2000). An adaptive clustering algorithm for color quantization. Pattern Recognition Letters, 21(4):337-346.
  11. Ilie, A. and Welch, G. (2005). Ensuring color consistency across multiple cameras. In IEEE Int. Conf. on Computer Vision, volume 2, pages 1268-1275.
  12. Kim, S. and Pollefeys, M. (2004). Radiometric selfalignement of image sequences. In Int. Conf. on Computer Vision and Pattern Recognition, volume 1, pages 645-651.
  13. Kim, S. and Pollefeys, M. (2008). Robust radiometric calibration and vignetting correction. IEEE Trans. on Pattern Analysis and Machine Intelligence, 30 (4):562- 576.
  14. Land, E. H. (1977). The retinex theory of color vision. Scientific American, pages 108-128.
  15. Mansouri, A., Marzani, F., and Gouton, P. (2005). Development of a protocol for ccd calibration: application to a multispectral imaging system. IJRA, 20(2):pp. 94- 100.
  16. McCann, J. (2004). Mechanism of color constancy. In IS&T/SID Conf. on Color Imaging, volume 12, pages 29-36.
  17. Mitsunaga, T. and Nayar, S. (1999). Radiometric selfcalibration. In Conf. on Vision and Pattern Recognition, volume 2,, pages 374-380.
  18. Porikli, F. (2003). Inter-camera color calibration by crosscorrelation model function. In IEEE Int. Conf. on Image Proc, volume 2, pages 133-136.
  19. Sirisathitkul, Y., Auwatanamongkol, S., and Uyyanonvara, B. (2004). Color image quantization using distances between adjacent colors along the color axis with highest color variance. Pattern Recognition Letters, 25(9):1025-1043.
  20. Wannous, H., Lucas, Y., Treuillet, S., and Albouy, B. (2008). A complete 3d wound assessment tool for accurate tissue classification and measurement. In Proc. 15th IEEE International Conference on Image Processing ICIP 2008, pages 2928-2931.
  21. Wannous, H., Treuillet, S., and Lucas, Y. (2007). Supervised tissue classification from color images for a complete wound assessment tool. In 29th Inter. Conf. of the IEEE Eng. in Med. and Bio. Soc. EMBS'07, pages 6031-6034.
Download


Paper Citation


in Harvard Style

Wannous H., Treuillet S., Lucas Y., Mansouri A. and Voisin Y. (2010). DESIGN OF A CUSTOMIZED PATTERN FOR IMPROVING COLOR CONSTANCY ACROSS CAMERA AND ILLUMINATION CHANGES . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-028-3, pages 60-67. DOI: 10.5220/0002835700600067


in Bibtex Style

@conference{visapp10,
author={Hazem Wannous and Sylvie Treuillet and Yves Lucas and Alamin Mansouri and Yvon Voisin},
title={DESIGN OF A CUSTOMIZED PATTERN FOR IMPROVING COLOR CONSTANCY ACROSS CAMERA AND ILLUMINATION CHANGES},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={60-67},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002835700600067},
isbn={978-989-674-028-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010)
TI - DESIGN OF A CUSTOMIZED PATTERN FOR IMPROVING COLOR CONSTANCY ACROSS CAMERA AND ILLUMINATION CHANGES
SN - 978-989-674-028-3
AU - Wannous H.
AU - Treuillet S.
AU - Lucas Y.
AU - Mansouri A.
AU - Voisin Y.
PY - 2010
SP - 60
EP - 67
DO - 10.5220/0002835700600067