Feature extraction methods for consistent spatio-temporal image sequence classification using hidden Markov models

In this paper a general and efficient approach for representing and classifying image sequences by hidden Markov models (HMMs) is presented. A consistent modeling of spatial and temporal information is achieved by extracting different low level image features, These implicitly convert the image intensities into probability density values, while preserving the geometry of the image. The resulting so called image density functions are contained in the states of the HMM. First results of applying the approach to the classification of dynamic hand gestures demonstrate the performance of the modeling