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The study of ``Computer Vision'' has been going on for about 30 years. Early work was done as part of the effort in ``Artificial Intelligence'' that took off in the 1970s. Researchers in pioneering centres such as MIT, Stanford and Edinburgh intended to get computers to reproduce human ability to see objects, recognise them and make sense of their movements. It all proved very much harder than anyone had anticipated. In the 1980s the field of Computer Vision sobered up. It became clear that a solid theoretical foundation was needed if any progress was going to be made. Ambitions needed to be toned down too. Before trying to replicate human abilities, it would be satisfying, and quite hard enough, to try to emulate the visual skills of a lizard or a bee, perhaps. It is the sheer breathtaking generality of the human ability to see that has been so difficult to capture. The most successful computer vision systems to date are ones that are strongly specialised: sorting potatoes or inspecting electrical circuit boards for instance. A key factor in recent successes in making computers ``see'' moving objects has been in getting the computer to anticipate. Suppose the computer is supposed to follow the trajectory of an intruder, captured on a security video. It helps enormously if the computer is programmed to expect a certain range of shapes (``roundish'') for the intruder's head. That information can be used to distinguish the true head from bits of visual ``flotsam'' in the room --- books on a shelve or pictures on the wall, for instance. Not only shape but also movement can be anticipated by the computer. The intruder's motion is expected to be smooth and largely horizontal (parallel to the floor). The ``prior'' information can be used to get the computer to concentrate, fixing on the moving head, without being continually distracted by the bright and attractive pieces of flotsam nearby. This can work successfully even when the motion is particularly vigorous, as with the dancing girl shown above. Tracking the blowing leaf, shown next, is an even more taxing problem because it is camouflaged -- the rest of the bush is conspiring to make things as hard as possible by imitating the selected foreground object. This whole art of programming anticipation into a computer is founded, perhaps surprisingly, on the mathematics of probability. This is because the shape of an object like the intruder's head is not known precisely in advance, but is constrained to lie in a certain probable range. Similarly the motion of the intruder is not known exactly in advance; that would beg the question that the computer vision system is supposed to answer. It is known, however, that only movements in a certain range are at all likely. Quantifying that range, in a way that is digestible by a computer program, can be done neatly using the ``language'' of probability.

The Computer Science vision, robotics, virtual reality and psychophysics laboratory is designed to support research on anthropomorphic robotic systems, human-machine interaction, and human performance. It currently consists of the following components.
•An ageing binocular head containing movable cameras for visual input.
•A new binocular head with computer controllable focus, aperture, and zoom.
•a 16 degree of freedom Utah dextrous manipulator or hand.
•two robot arms that can support and move the head and hand.
•ExOS, Dataglove, and Flock of Birds devices for digitizing hand and joint positions (human and robotic) and teleoperation research.
•an aging special-purpose pipeline parallel processor for high-bandwidth low-level vision processing.
•A new video pipeline (Max200).
•General-purpose MIMD parallel computation (8-node SPARCcenter 2000, 12-node SGI Challenge).
•SGI Crimson processor dedicated to graphics and virtual reality, with GTX graphics hardware.
•Binocular virtual reality helmet, specially modified to include eye-tracking capabilities inside the helmet.
•Instrumented ``car'' (a go-kart frame) with outputs from steering wheel, accelerator and brake position.
•A head-mounted eye-tracking device (including A Flock of Birds sensors for head and hand position). It is an Applied Scientific Laboratories Series 4000, with the helmet and head-tracker option. It can track eye movements in head coordinates, and in conjunction with the Flock of Birds head position sensor it can quantitatively track gaze directions in LAB coordinates.
•High-quality 8mm editing tape deck for recording eye-movement data.