Robust color segmentation using the dichromatic reflection model

This paper proposes a robust color segmentation method for real-world scenes. The robustness comes from a physics-based color analysis and the use of robust statistics. We analyze data distributions in the RGB color space to identify (non-Gaussian) object color clusters which conform to the dichromatic reflection model. Such a physics-based approach enables the detection of diffusion and specular interface reflections as well as body reflection, whose mixtures often confuse traditional statistics-based color segmentation algorithms. Experimental results show that accurate image segmentation can be realized and object colors can be correctly estimated without bias