Abstract
In this paper a fast method to recognize image sequences is presented. It is based on a discrete statistical model consisting of a vector quantizer and a special probabilistic neural net giving an estimation for the a posteriori probability P(SEQUENCE|DATA), which allows to classify image sequences without applying rules depending on the content of the sequence. The simple feature extraction also allows the classification with discrete Hidden Markov Models. As an application we present results from a test conducted for the classification of various gestures done by human beings in front of a video camera for both classification methods, which gave promising recognition results in real time.
| Original language | English |
|---|---|
| Pages (from-to) | 3450-3453 |
| Number of pages | 4 |
| Journal | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
| Volume | 6 |
| State | Published - 1996 |
| Externally published | Yes |
| Event | Proceedings of the 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 6) - Atlanta, GA, USA Duration: 7 May 1996 → 10 May 1996 |