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BOUNDARY ESTIMATES OF AN SIGNAL CLASSIFICATION EFFICIENCY
Abstract
The key objective of modern vision systems is the problem of a signal class determining since the signal features are completely determined by the type of underlying surface relative to which the observation is made. In spite of the fact that at present there are a huge number of analytic methods for the solving the indicated problem, in the vast majority of tasks based on natural images, capacities of solving it are characterized by the probabilities of erroneous and proper decisions. The exact analytic probability estimate can be calculated in particular cases only. Under more common conditions, it is advisable to use Kailath boundary ratio for a normalization of probability scores obtaining for system processing characteristics. The Kailath boundary ratio is considered both for the case of two hypotheses, and for the case of adjective hypotheses. The obtained results are illustrated for some specific examples, which deal with recognition tasks with application of radar images.
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