Watershed-driven relaxation labeling for image segmentation

Introduces an image segmentation method referred to as watershed-driven relaxation labeling. The method is a hybrid segmentation process utilizing both watershed analysis and relaxation labeling. Initially, watershed analysis is used to subdivide an image into catchment basins, effectively clustering pixels together based on their spatial proximity and intensity homogeneity. Classification estimates in the form of probabilities are set for each of these catchment basins. Relaxation labeling is then used to iteratively refine and update the classifications of the catchment basins through propagating constraints and utilizing local information. The relaxation updating process is continued until a large majority of the catchment basins are unambiguously classified. The method provides fast, accurate segmentation results and exploits the individual strengths of watershed analysis and relaxation labeling. The robustness of the method is illustrated through comparisons to other popular segmentation techniques