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Abstract

Currently two major limitations to applying vision in real tasks are robustness in real-world, uncontrolled environments, and the computational resources required for real-time operation. In particular, many current
robotic visual motion detection algorithms (optical flow) are not suited for practical applications such as segmentation and structure-from-motion because they either require highly specialized hardware or up to several
minutes on a scientific workstation. In addition, many such algorithms depend on the computation of first and in some cases higher numerical derivatives, which are notoriously sensitive to noise. In fact the current
trend in optical flow research is to stress accuracy under ideal conditions and not to consider computational resource requirements or resistance to noise, which are essential for real-time robotics. As a result robotic vision researchers are frustrated by an inability to obtain reliable optical flow estimates in real-world conditions, and practical applications for optical flow algorithms remain scarce. Algorithms based on the correlation of image patches have been shown to be robust in practice but
are in general infeasible due to their computational complexity. This paper describes a space-time tradeoff to this algorithm which converts a quadratic-time algorithm into a linear-time one, as well as a method for dealing with the resulting problem of temporal aliasing, resulting in an algorithm that can run at over 6 frames per second on an 50 MHz Sun Sparcstation 20.