Color cast detection based on color by correlation and color constancy algorithm using kernel density estimation

색 상관 관계 기반의 색조 검출 및 핵밀도 추정을 이용한 색 항상성 알고리즘

  • Received : 2009.11.12
  • Accepted : 2010.04.01
  • Published : 2010.04.30

Abstract

Digital images have undesired color casts due to various illumination conditions and intrinsic characteristics of cameras. Since the color casts in the images deteriorate performance of color representations, color correction is required for further analysis of images. In this paper, an algorithm for detection and removal of color casts is presented. The proposed algorithm consists of four steps: retrieving similar image using color by correlation, extraction of near neutral color regions, kernel density estimation, and removal of color casts. Ambiguities in near neutral color regions are excluded based on kernel density estimation by the color by correlation algorithm. The method determines whether there are color casts by chromaticity distributions in near neutral color regions, and removes color casts for color constancy. Experimental results suggest that the proposed method outperforms the gray world algorithm and the color by correlation algorithm.

디지털 영상은 조명 조건과 취득 카메라의 고유 특성으로 인해 의도하지 않은 색조를 가질 수 있다. 영상에 색조가 존재하면 일관된 색 정보의 인지 및 표현이 어렵기 때문에 별도의 색 보정 작업이 필요하다. 본 논문은 color by correlation을 사용한 학습 영상 선택, 후보 회색축 영역의 추출, 핵밀도 추정, 색조 제거의 4단계로 이루어진 색조 추출 및 제거 방법을 제안한다. 후보 회색축 영역 중 불명확한 회색축 영역을 핵밀도 추정을 이용하여 제거하였다. 후보 회색축 영역의 색 성분의 분포를 조사하여 색조 유무를 판단하고, 색조가 존재할 경우 색조 제거 작업을 통하여 색 항상성을 유지 시켰다. 실험을 통해 제안하는 방법이 gray world 방법, color by correlation 방법 보다 정확한 색조 추정이 가능함을 확인하였다.

Keywords

References

  1. K. Barnard, V. Cardei, and B. Funt, "A comparison of computational color constancy algorithmspart I: Methodology and experiments with syntetized data," IEEE Transactions on Image Processing, Vol.11, No.9, pp. 972-984, 2002. https://doi.org/10.1109/TIP.2002.802531
  2. T. Yanghai, R.T. Collins, V. Ramesh, and T. Kanade, "Bayesian color constancy for outdoor object recognition," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Vol.1, pp. I-1132-I-1139, 2001.
  3. A. Nayak and S. Chaudhuri, "Automatic illumination correction for scene enhancement and object tracking," Image and Vision Computing, Vol.24, No.9, 2006.
  4. J.P. Renno, D. Makris, T. Ellis, and G.A. Jones, "Application and evaluation of colour constancy in visual surveillance," Proceedings of International Conference on Computer Communications and Networks, pp. 301-308, 2005.
  5. G.D. Finlayson, S.D. Hordley, and P.M. Hubel, "Color by correlation: A simple, unifying framework for color constancy," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.23, No.11, pp. 1209-1221, 2001. https://doi.org/10.1109/34.969113
  6. T.J. Cooper, I. Tastl, and B. Tao, "Novel approach to color cast detection and removal in digital images," Proceedings of SPIE, Vol. 3963, pp.167-177, 1999.
  7. F. Gasparini and R. Schettini, "Color correction for digital photographs," Proceedings of International Conference on Image Analysis and Processing, pp. 646-651, 2003.
  8. G. Buchsbaum, "A spatial processor model for object colour perception," Journal of the Franklin Institute, Vol.310, No.1, pp.1-26, 1980. https://doi.org/10.1016/0016-0032(80)90058-7
  9. K. Barnard, "Practical color constancy," PhD thesis, School of Computing, Simon Fraser University, 1999.
  10. K.N. Plataniotis and A.N. Venetsanopoulos, Color image processing and applications, Springer, New York, 2000.
  11. R.O. Duda, P.E. Hart, and D.G. Stork, Pattern classification, 2nd ed, Wiley, New York, 2001.
  12. F. Gasparini and R. Schettini, "Color balancing of digital photos using simple image statistics," Pattern Recognition Vol.37, No.6, pp. 1201-1217, 2004. https://doi.org/10.1016/j.patcog.2003.12.007
  13. K. Barnard, V. Cardei, and B. Funt, "A comparison of computational color constancy algorithms- part II: Experiments with image data," IEEE Transactions on Image Processing, Vol.11, No.9, pp.985-996, 2002. https://doi.org/10.1109/TIP.2002.802529
  14. K. Barnard, L. Martin, B. Funt, and A. Coath, "A data set for color research," Color Research and Application, Vol.27, No.3, pp.147-151, 2002. https://doi.org/10.1002/col.10049