An HMM-based mmodel for moving object detection
| dc.contributor.author | A.D Debilou | |
| dc.contributor.author | Aouragh Salima | |
| dc.date.accessioned | 2024-02-14T07:50:39Z | |
| dc.date.available | 2024-02-14T07:50:39Z | |
| dc.date.issued | 2006 | |
| dc.description | This study introduces a new segmentation method based on hidden Markov Models. | |
| dc.description.abstract | A new probabilistic background-foreground model based on Hidden Markov Models (HMMs) is presented. The hidden states of the model enable discrimination between foreground and background. This method is composed of two phases: first, an ICE (Iterative Conditional Estimation) is introduced to learn the unknown HMM parameters. In the second stage, each pixel is classified with MPM (Maximum Posterior Marginal) classification algorithm. The potential and efficiency of the method have been proven through simulations under Matlab. | |
| dc.identifier.uri | http://dspace.univ-khenchela.dz:4000/handle/123456789/845 | |
| dc.publisher | Asian journal of information technology | |
| dc.title | An HMM-based mmodel for moving object detection |
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