Making a computer see was something that leading experts in the field of Artificial Intelligence thought to be at the level of difficulty of a summer student's project back in the sixties. Forty years later the task is still unsolved and seems formidable. A whole field, called Computer Vision, has emerged as a discipline in itself with strong connections to mathematics and computer science and looser connections to physics, the psychology of perception and the neuro sciences.

One of the likely reasons for this half-failure is the fact that researchers had over-looked the fact, perhaps because of this plague called naive introspection, that perception in general and visual perception in particular are far more complex in animals and humans than was initially thought. There is of course no reason why we should pattern Computer Vision algorithms after biological ones, but the fact of the matter is that

1. (i) the way biological vision works is still largely unknown and therefore hard to emulate on computers, and

2. (ii) attempts to ignore biological vision and reinvent a sort of silicon-based vision have not been so successful as initially expected.

Despite these negative remarks, Computer Vision researchers have obtained some outstanding successes, both practical and theoretical.

On the side of practice, and to single out one example, the possibility of guiding vehicles such as cars and trucks on regular roads or on rough terrain using computer vision technology was demonstrated many years ago in Europe, the USA and Japan.