Journal article
Partially Hidden Markov Models
Partially Hidden Markov Models (PHMM) are introduced. They differ from the ordinary HMM's in that both the transition probabilities of the hidden states and the output probabilities are conditioned on past observations. As an illustration they are applied to black and white image compression where the hidden variables may be interpreted as representing noncausal pixels.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 1996 |
| Pages: | 1253-1256 |
| ISSN: | 15579654 and 00189448 |
| Types: | Journal article |
| DOI: | 10.1109/18.508852 |
| ORCIDs: | Forchhammer, Søren Otto |
Councils Data compression Hidden Markov models Image coding Parameter estimation Pixel Probability distribution Speech recognition State-space methods Text recognition black and white image compression data compression hidden Markov models hidden states image coding image representation noncausal pixels output probabilities partially hidden Markov models past observations probability transition probabilities
