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Proceedings Paper

Spatiotemporal pattern recognition using hidden Markov models
Author(s): Kenneth H. Fielding; Dennis W. Ruck; Steven K. Rogers; Byron M. Welsh; Mark E. Oxley
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Paper Abstract

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.

Paper Details

Date Published: 29 October 1993
PDF: 11 pages
Proc. SPIE 2032, Neural and Stochastic Methods in Image and Signal Processing II, (29 October 1993); doi: 10.1117/12.162031
Show Author Affiliations
Kenneth H. Fielding, Air Force Institute of Technology (United States)
Dennis W. Ruck, Air Force Institute of Technology (United States)
Steven K. Rogers, Air Force Institute of Technology (United States)
Byron M. Welsh, Air Force Institute of Technology (United States)
Mark E. Oxley, Air Force Institute of Technology (United States)


Published in SPIE Proceedings Vol. 2032:
Neural and Stochastic Methods in Image and Signal Processing II
Su-Shing Chen, Editor(s)

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