An improved evolutionary algorithm for fundamental matrix estimation

The estimation of the fundamental matrix is an important problem in epipolar geometry. Many estimation methods have been proposed before, including the eight-point algorithm, Simple Evolutionary Agent (SEA) and RANSAC. In this paper, we investigate the evolutionary agent-based algorithm for fundamental matrix estimation, and present a new algorithm that improves the existing evolutionary algorithm both accuracy- and efficiency-wise. The model focuses on selecting a best combination of input points to compute the fundamental matrix via the eight-point algorithm. To improve the existing algorithm, our new model holds competition over all agents for population control and evolutionary experience accumulation. In addition to a larger competition scope, we add the outlier elimination mechanism, which greatly accelerates the algorithm. New parameters are introduced to control the convergence more efficiently. The improved algorithm achieves lower computation load and more accurate results. A general analysis about parameter selection is also provided.