Highlight and Shading Invariant Color Image Segmentation Using Simulated Annealing

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 MRF segmentation. Results are presented and analyzed.

[1]  B. Wandell.  Foundations of vision , 1995 .

[2]  Shoji Tominaga,et al.  Dichromatic reflection models for a variety of materials , 1994 .

[3]  Alain Trémeau,et al.  A region growing and merging algorithm to color segmentation , 1997, Pattern Recognit..

[4]  Shoji Tominaga.  Spectral imaging by a multichannel camera , 1999, J. Electronic Imaging.

[5]  Robert J. Schalkoff,et al.  Pattern recognition - statistical, structural and neural approaches , 1991 .

[6]  S. Haykin,et al.  Image Segmentation Using A Mixture Of Principal Components Representation , 1997 .

[7]  Sang Uk Lee,et al.  On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques , 1990, Pattern Recognit..

[8]  Sang Uk Lee,et al.  Color image segmentation based on 3-D clustering: morphological approach , 1998, Pattern Recognit..

[9]  S. Li.  Modeling Image Analysis Problems Using Markov Random Fields , 2000 .

[10]  Steven A. Shafer,et al.  Using color to separate reflection components , 1985 .

[11]  W.V. Stoecker,et al.  Boundary detection in skin tumor images: An overall approach and a radial search algorithm , 1990, Pattern Recognit..

[12]  S. Tominaga.  Color classification of natural color images , 1992 .

[13]  B. Wandell,et al.  Standard surface-reflectance model and illuminant estimation , 1989 .

[14]  Robert Dony,et al.  Global color image segmentation strategies: Euclidean distance vs. vector angle , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[15]  Shoji Tominaga,et al.  Spectral imaging by a multichannel camera , 1998, Electronic Imaging.

[16]  Shoji Tominaga,et al.  Shading- and highlight-invariant color image segmentation using the MPC algorithm , 2000, IS&T/SPIE Electronic Imaging.

[17]  Bart Kosko,et al.  Neural networks for signal processing , 1992 .

[18]  Shoji Tominaga,et al.  Surface Identification Using the Dichromatic Reflection Model , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Donald Geman,et al.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .

[20]  Glenn Healey,et al.  Markov Random Field Models for Unsupervised Segmentation of Textured Color Images , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Peter J. W. Rayner,et al.  Unsupervised image segmentation using Markov random field models , 1997, Pattern Recognit..

[22]  Anil K. Jain,et al.  Markov random fields : theory and application , 1993 .

[23]  Slawomir Wesolkowski,et al.  Color Image Edge Detection and Segmentation: A Comparison of the Vector Angle and the Euclidean Distance Color Similarity Measures , 1999 .

[24]  Chengyi Sun,et al.  Color image segmentation and understanding through connected components , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[25]  Huang Yumin,et al.  A PHYSICAL APPROACH TO COLOR IMAGE UNDERSTANDING , 1991 .

[26]  Gerhard Winkler,et al.  Image analysis, random fields and dynamic Monte Carlo methods: a mathematical introduction , 1995, Applications of mathematics.

[27]  Josef Kittler,et al.  Automatic watershed segmentation of randomly textured color images , 1997, IEEE Trans. Image Process..