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This paper presents a mathematical basis for establishing achievable performance levels for multisensor electronic vision systems.A random process model of the multisensor scene environment is developed. The concept of feature space and its importance in the context of this model is presented.A set of complexity metrics used to measure the difficulty of an electronic vision task in a given scene environment is developed and presented.These metrics are based on the feature space used for the electronic vision task and the a priori knowledge of scene truth.Several applications of complexity metrics to the analysis of electronic vision systems are proposed.

A new K-nearest neighbor (KNN) statistic is introduced to fuse information from multiple sensors/features into a single dimensional decision space for electronic vision systems. Theorems establish the relationship of the KNN statistic to other probability density function distance measures such as the Kolmogorov-Smirnov Distance and the Tie Statistic. A new KNN search algorithm is presented along with factors for selecting K. Applications include cueing and texture recognition.

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