Abstract
Color constancy in color image segmentation is an important research issue. In this paper we develop a framework, based on the Dichromatic Reflection Model for asserting the color highlight and shading invariance, and based on a Markov Random Field approach for segmentation. A given RGB image is transformed into a R’G’B’ space To remove any highlight components, and only the vector-angle component, representing color hue but not intensity, is preserved to remove shading effects. Due to the arbitrariness of vector angles for low R’G’B’ values, We perform a Monte-Carlo sensitivity analysis to determine pixel-dependent weights for the MR F segmentation. Results are presented and analyzed.
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References
S. A. Barker, and P. J. W. Rayner, “Unsupervised Image Segmentation Using Markov Random Fields,” in M. Pelillo and E. R. Hancock (ed), Energy Minimization Methods in Computer Vision and Pattern Recognition, pp. 165–178, Springer-Verlag: 1997.
R. Chellappa, and A. Jain, Markov Random Fields: Theory and Application. Academic Press, New York, 1993.
R. D. Dony, and S. Haykin, “Image segmentation using a mixture of principal components representation,” IEE Proc. VISP, vol. 144, pp. 73–80, April 1997.
S. Geman and D. Geman, “Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images,” IEEE Trans-PAMI, Vol. 6, No. 6, 1984.
G.J. Klinker, S.A. Shafer and T. Kanade, “A Physical Approach to Color Image Understanding,” Inter’l J. of Computer Vision, Vol. 4, No. 1, pp. 7–38, 1990.
S. Z. Li, “Modeling Image Analysis Problems Using Markov Random Fields,” in C.R. Rao and D.N. Shanbhag (ed), Stochastic Processes: Modeling and Simulation, Vol. 20 of Handbook of Statistics. Elsevier Science, 2000, pp. 1–43.
Y.W. Lim, and S.U. Lee, “On the color image segmentation algorithm based on the thresholding and fuzzy c-means techniques,” Pattern Recognition, vol. 23, no. 9, pp. 1235–1252, 1990.
D. K. Panjwani, and G. Healey, “Markov Random Field Models for Unsupervised Segmentation of Textured Color Images,” IEEE Trans-PAMI, Vol. 17, No. 10, 1995.
S. H. Park, I. D. Yun, and S.U. Lee, “Color Image Segmentation Based on 3-D Clustering: Morphological Approach,” Pattern Recognition, vol. 31, no. 8, pp. 1061–1076, 1998.
R.J. Schalkoff, Pattern Recognition: Statistical, Structural and Neural Approaches. John Wiley & Sons, Inc., New York, 1992.
L. Shafarenko, M. Petrou, and J. Kittler, “Automatic watershed segmentation of randomly textured color images,” IEEE Trans. on Image Processing, vol. 6, pp. 1530–1544, November 1997.
S.A. Shafer, “Using color to separate reflection components,” TR-136, Computer Sciences Dept., University of Rochester, NY, April 1984.
S. Tominaga and B. Wandell, “The standard reflectance model and illuminant estimation”, J. of Optical Society of America A, Vol. 6, No. 4, pp. 576–584, April 1989.
S. Tominaga, “Surface Identification Using the Dichromatic Reflection Model,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 13, No. 7, pp. 658–670, July 1991.
S. Tominaga, “Color Classification of Natural Color Images,” Color Research and Application, Vol. 17, No. 4, pp. 230–239, 1992.
S. Tominaga, “Dichromatic Reflection Models for a Variety of Materials,” Color Research and Application, Vol. 19, No. 4, pp. 277–285, 1994.
S. Tominaga, “Spectral imaging by a multichannel camera,” Journal of Electronic Imaging, vol. 8, no. 4, pp. 332–342, 1999.
A. Tremeau, and N. Borel, “A Region Growing and Merging Algorithm to Color Segmentation,” Pattern Recognition, vol. 30, no. 7, pp. 1191–1203, 1997.
B.A. Wandell. Foundations of Vision, Sinauer Associates, Inc. Publishers, Sunderland, MA, 1995.
W. Wang, C. Sun, and H. Chao, “Color Image Segmentation and Understanding through Connected Components,” IEEE International Conference on Systems, Man, and Cybernetics, vol. 2, pp. 1089–1093, October 1997.
S. Wesolkowski, M.E. Jernigan, R.D. Dony, “Global Color Image Segmentation Strategies: Euclidean Distance vs. Vector Angle,” in Y.-H. Hu, J. Larsen, E. Wilson and S. Douglas (eds.), Neural Networks for Signal Processing IX, IEEE Press, Piscataway, NJ, 1999, pp. 419–428.
S. Wesolkowski, Color Image Edge Detection and Segmentation: A Comparison of the Vector Angle and the Euclidean Distance Color Similarity Measures, Master’s thesis, Systems Design Engineering, University of Waterloo, Canada, 1999.
S. Wesolkowski, S. Tominaga, and R.D. Dony, “Shading and Highlight Invariant Color Image Segmentation Using the MPC Algorithm,” SPIE Color Imaging:Device-Independent Color, Color Hardcopy, and Graphic Arts VI, San Jose, USA, January 2001, pp. 229–240.
G. Winkler, Image Analysis, Random Fields and Dynamic Monte Carlo Methods, Springer-Verlag, Berlin, Germany, 1995.
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Fieguth, P., Wesolkowski, S. (2001). Highlight and Shading Invariant Color Image Segmentation Using Simulated Annealing. In: Figueiredo, M., Zerubia, J., Jain, A.K. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2001. Lecture Notes in Computer Science, vol 2134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44745-8_21
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DOI: https://doi.org/10.1007/3-540-44745-8_21
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