Nonlinear Relaxation Labeling for Image Processing

In the attached .zip folder you the main, self-contained function titled RelaxLabel2D which performs nonlinear relaxation labeling of 2D monochromatic and multispectral images. Also included are three demo files:

RelaxLabel2D_demo1: Provides an example on how to regularize binary images corrupted by spurious noise artifacts.

RelaxLabel2D_demo2: Shows how to segment color images using k-means clustering and then regularize the result by probabilistic relaxation. This demo uses my own implementation of bisecting k-means, which can provide robust and consistent initialization of cluster centroids.

RelaxLabel2D_demo3: Shows how to segment grayscale images into background and foreground regions with the help of probabilistic relaxation.

REFERENCES:
[1] Eklundh, J.O., Yamamoto, H., Rosenfeld, A. (1980) 'A relaxation method for multispectral pixel classification', IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-2, pp.72-75.
[2] Kittler, J., Illingworth, J. (1985) 'Relaxation labelling algorithms a review', Image and Vision Computing, Vol.3, pp.206-216.
[3] Peleg, S., Rosenfeld, A. (1978) 'Determining compatibility coefficients for curve enhancement relaxation processes', IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-8, pp.548-555.