We place scaled and rotated versions of the template into the background image and match image with the original target. We form a match-quality space that records the quality of match at each location in the image. We then a model to the match-quality space and use the amplitude and variance of these functions to quantify the quality of match. J. D. Crisman, Y. Du, and M. Cleary, September 1993. Invited by N. Wittels.
Humans accomplish the analogous task by positioning their heads and eyes for optimal stereo effects.
This paper describes recent work toward developing automated control strategies for camera motion in stereo machine vision systems for mobile robot navigation. Our goal is to achieve fast, reliable pursuit of a target while avoiding obstacles. Our strategy results in smooth, stable camera motion despite robot and target motion. Our algorithm has been shown to be successful at navigating a mobile robot, mediating visual target tracking and ultrasonic obstacle detection. J. Ayers and J. D. Crisman.