A self-organizing lattice Boltzmann active contour (SOLBAC) approach for fast and robust object region segmentation

In this paper, we propose a self-organized learning based active contour model with a lattice Boltzmann convergence criteria for fast and effective segmentation preserving the precise details of the object´s region of interest. A dual self-organizing map approach is being used to learn the object of interest and the background independently in order to guide the active contour to extract the target region. The lattice Boltzmann method is utilized to evolve the level-set function faster and terminate the evolution of the curve at the most optimum region, which segments objects in cluttered environments. Experiments performed on a challenging dataset (PSCAL 2011) show promising results in terms of time and quality of the segmentation and that our method is more than 53% faster than other state-of-the-art learning-based active contour model approaches.