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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chardin, A. Perez, P. |
| Copyright Year | 1999 |
| Description | Author affiliation: IRISA, Rennes, France (Chardin, A.) |
| Abstract | This work takes place in the context of hierarchical stochastic models for the resolution of discrete inverse problems from low level vision. We investigate a new hybrid hierarchical structure: a Markov random field attached to the nodes of a truncated tree. It thus combines causal hierarchical prior on trees with a non-causal spatial prior at the coarsest level. We address the problem of computing posterior marginals with such a prior structure. This is performed using non-iterative two-sweep marginalizations on trees, combined with a low cost Gibbs sampler in between the two sweeps. Posterior marginals are thus obtained in a semi-iterative way. They are then used to infer unknown variables according to the IMPM estimator. This is illustrated by experiments on synthetic data and on multispectral satellite images. |
| Starting Page | 324 |
| Ending Page | 328 |
| File Size | 520643 |
| Page Count | 5 |
| File Format | |
| ISBN | 0780354672 |
| DOI | 10.1109/ICIP.1999.821623 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1999-10-24 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Stochastic processes Inverse problems Iris Context modeling Spatial resolution Costs Satellites Image analysis Energy management Energy capture |
| Content Type | Text |
| Resource Type | Article |
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