Detection of new image objects in video sequences using neural networks

The detection of image segmented objects in video sequences is constrained by the a priori information available with a classifier. An object recogniser 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 recogniser. Neural networks can be used to develop a strategy to automatically recognise new objects in image scenes that can be separated from other data for manual labelling. 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 labelling of new objects in scene analysis.