Minimization of an energy function with robust features for image segmentation

Abstract

In this work, we propose an approach to the model based on Markov random field (MRF) as a systematic way for integrating constraints for robust image segmentation. To do that, robust features and their integration in the energy function, which directs the process, have been defined. The suitability of the method has been verified by comparing classic features with the robust ones. In this approach, the image is first segmented into a set of disjoint regions and the adjacent graph (AG) has been determined. This approach is applied by defining an MRF model on the corresponding AG. Robust features are incorporated to the energy function by means of clique functions, and optimal segmentation is then achieved by finding a labelling configuration, which minimizes the energy function using the simulated annealing.

Citation

Arques, P., Compañ, P., Molina, R., Pujol, M. and Rizo, R. (2003), "Minimization of an energy function with robust features for image segmentation", Kybernetes, Vol. 32 No. 9/10, pp. 1481-1491. https://doi.org/10.1108/03684920310493378