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29 October 1993 Spatiotemporal pattern recognition using hidden Markov models
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A spatio-temporal method for identifying objects contained in an image sequence is presented. The Hidden Markov Model (HMM) technique is used as the classification algorithm, making classification decisions based on a spatio-temporal sequence of observed object features. A five class problem is considered. Classification accuracies of 100% and 99.7% are obtained for sequences of images generated over two separate regions of viewing positions. HMMs trained on image sequences of the objects moving in opposite directions showed a 98.1% successful classification rate by class and direction of movement. The HMM technique proved robust to image corruption with additive correlated noise and had a higher accuracy than a single look nearest neighbor method. A real image sequence of one of the objects used was successfully recognized with the HMMs trained on synthetic data. This study shows the temporal changes that observed feature vectors undergo due to object motion hold information that can yield superior classification accuracy when compared to single frame techniques.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kenneth H. Fielding, Dennis W. Ruck, Steven K. Rogers, Byron M. Welsh, and Mark E. Oxley "Spatiotemporal pattern recognition using hidden Markov models", Proc. SPIE 2032, Neural and Stochastic Methods in Image and Signal Processing II, (29 October 1993);

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