Correlation based Vergence Control using Log - polar Images ?

This paper describes a real-time vergence control mechanism based on log-polar images, developed for a robot head. We show that vergence behavior can be achieved at reduced computational cost using simple correlation measures on log-polar images. The main advantages of using a non-uniform image sampling mechanism, such as the log-polar images, are related both to perceptual and algorithm complexity issues. We show that, when using correlation measures to control vergence, log-polar images give better results than cartesian images. Additionally, as log-polar images are smaller, the computation time is reduced. Two algorithms for closed loop vergence control, using correlation measures over log-polar images, are proposed and compared. Their behavior in real situations is illustrated by test examples.