Abstract
The detection of image segmented objects in video sequences is constrained by the a priori information available with a classifier. An object recognizer labels image regions based on texture and shape information about objects for which historical data is available. The introduction of a new object would culminate in its misclassification as the closest possible object known to the recognizer. Neural networks can be used to develop a strategy to automatically recognize new objects in image scenes that can be separated from other data for manual labeling. In this paper, one such strategy is presented for natural scene analysis of FLIR images. Appropriate threshold tests for classification are developed for separating known from unknown information. The results show that very high success rates can be obtained using neural networks for the labeling of new objects in scene analysis.
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