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

In this paper a general and e cient approach for representing and classifying image sequences by Hid den Markov Models HMMs is presented A consis tent modeling of spatial and temporal information is achieved by extracting di erent low level image fea tures These implicitly convert the image intensities into probability density values while preserving the ge ometry of the image The resulting so called image den sity functions are contained in the states of the HMM First results of applying the approach to the classi ca tion of dynamic hand gestures demonstrate the perfor mance of the modeling