Abstract
Most methods of pattern recognition consider objects at a fixed moment in time without taking into account their temporal development. However, there are a lot of applications in which the order of state changes of an object over time determines its membership to a certain pattern, or class. In these cases, for the correct recognition of objects it is very important not only to consider properties of objects at a certain moment in time but also to analyse properties characterising their temporal development. This means that the history of temporal development of an object has a strong effect on the result of the recognition process. Classical methods of pattern recognition are not suitable for processing objects described by temporal sequences of observations. In order to deal with problems in which a dynamic viewpoint is desirable, methods of dynamic pattern recognition must be applied.
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