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A qualitative comparison of the performance of nine different segmentation algorithms on a database of infrared images of vehicles is described. The segmentation methods are categorised according to their mode of operation into three distinct generic classes of algorithm: namely 'grey level threshold techniques', 'three dimensional histogram methods' and 'pixel classification techniques'. Each segmentation technique is guided to a subset of the image by a spoke filter detection algorithm which locates regions of the scene that most resemble blob shaped man-made objects. A short list of four segmentation algorithms is compiled, of which two methods from the 'pixel classification' class, a K-nearest neighbour (KNN) and a Bayesian algorithm, are selected. The final preference is for the Bayesian technique, the KNN method being less favoured owing to the higher computational burden.