Proceedings,
Knowledge-Based Computer Vision
volume 196 of Dagstuhl-Seminar-Report, (1997)
Abstract
Computer vision includes both biological and engineering goals and it is important to specify towards which field the research is directed at it determines how one should evaluate the results. Computer vision should exploit existing theories and techniques from other sciences. This includes fields like physics, biology, computer science, artificial intelligence, statistics and control engineering. Time/dynamics has only recently been recognised as an important aspect of building operational systems. This is partly due to the fact that construction of fully operational system only recently has become possible. In addition methods for description of dynamics at several different levels from control theory to temporal logic have only recently been integrated into a coherent framework. The combination of different disciplines has only happened recently which in part is due to the fact that there has been a kind of 'religious' separation between fields like geometry, pattern recognition, control theory and artificial intelligence. I.e., simple applications, for example in pattern recognition, were not considered computer vision. In the view of complete systems it is, however, now apparent that such systems can only be built when the disciplines are combined with proper use of a multi-disciplinary approach. The issue of adequate computer power was discussed. It is not immediately obvious if we have enough computing power to solve current problems. A more important problem might, however, be adequate knowledge. Most systems developed today use little or no explicit knowledge. Another related problem is that almost no systems have a well characterised knowledge base, which implies that the systems can not be combined with methods for learning and/or adaptation.
