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In this paper we present a new method for the robust estimation of the trifocal tensor, from a series of medical images, using finite-multiple evolutionary agents. Each agent denotes a subset of matching points for parameter estimation, and the dataset of correspondences is considered as the environment in which the agents inhabit, evolve and execute some evolutionary behavior. Survival-of-finite-fitness rule is employed to keep the dramatic increase of new agents within limits, and reduce the chance of reproducing unfit ones. Experiments show that our approach performs better than the typical methods in terms of accuracy and speed, and is robust to noise and outliers even when a large number of outliers are involved.

MULTIPLE VIEW GEOMETRY ESTIMATION BASED ON FINITE-MULTIPLE EVOLUTIONARY AGENTS FOR MEDICAL IMAGES