An accurate multi-cell parameter estimate algorithm with heuristically restrictive ant system

To reliably analyze multi-cell motion in a series of low-contrast image sequences, we present a novel heuristically restrictive ant system, which operates in a non-optimization way, to adaptively estimate multiple parameters of multiple cells. First, the local intensity variation measure on each pixel of image is defined to generate ant colony initial distribution positions, which are further treated as boundary markers to restrict ant searching behavior. Afterwards, to speed up the ant searching process, both location and contour ant decision behaviors are modeled appropriately to acquire cell position and edge estimates on their individual pheromone fields, which are formed by restrictive pheromone deposits but operate independently and in parallel. Finally, the stability of our proposed pheromone control mechanism is proven to guarantee reliable multi-parameter extraction. Experiment results show that our algorithm could automatically and accurately track numerous cells in various scenarios, and it shows considerable robustness against other popular tracking methods.