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Contextual Analysis Of Tactical Scenes

Current automated approaches for recognizing spatially localized objects fall short of human recognition performance for two reasons. First, the classical statistical pattern recognizer uses only locally measured scene information in the recognition process. Contextual information can and should be extracted and exploited to enhance automated statistical approaches. Second, the human recognizer is able to learn from and adapt to new situations, while the automatic statistical pattern recognizer is confounded by any object that fails to match some subset of its training experience. Techniques from artificial intelligence can be employed to endow the machine with the ability to adapt to the scene at hand based on feedback of derived scene information. This paper describes an intelligent automated object recognition system that uses extracted scene context and feedback for improved recognition.