Integrating spatio-temporal information in image sequence analysis to enforce consistency of interpretation

We present a technique of image-sequence analysis which ensures consistency of interpretation in terms of the classification of segmented regions, both spatially within an image and temporally through a sequence of forward-looking infrared images taken from a low-flying aircraft. The technique is based on classical relaxation labeling techniques but is novel in that it treats segmented regions as single entities that have both spatial and temporal neighbors. This enables classification probabilities both to be propagated through the sequence and to be used in the minimization of the Kullback entropy of information. Sample results are shown from the application of the method to a 12-s sequence of infrared images that have been segmented using co-occurrence-based techniques. For completeness, the paper also describes the whole analysis algorithm including segmentation, texture analysis using discrete Hermite functions, and classification using neural network techniques, (C) British Crown copyright 1998.