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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Devijver, P.A. |
| Copyright Year | 1990 |
| Description | Author affiliation: Ecole Nat. Superieure des Telecommon. de Bretagne, Brest, France (Devijver, P.A.) |
| Abstract | The image modeling problem is discussed under the assumption that images can be represented by third-order. hidden Markov mesh random field models. The modeling applications comprise restoration of binary images, compression of image data, and segmentation of gray-level images and image sequences under the short-range motion hypothesis. Coherent approaches to the problems of image modeling and estimation of model parameters are outlined. A labeling algorithm based on a maximum marginal a posteriori probability criterion is proposed. Critical aspects of the computer simulation of a real-time implementation are discussed in detail. A learning technique by which the model parameters can be estimated without ground truth information is developed. Extensive experimentation with both static and dynamic images from a variety of sources is discussed. |
| Starting Page | 194 |
| Ending Page | 199 |
| File Size | 686862 |
| Page Count | 6 |
| File Format | |
| ISBN | 0818620625 |
| DOI | 10.1109/ICPR.1990.119353 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1990-06-16 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Hidden Markov models Image sequences Labeling Parameter estimation Image restoration Image coding Image segmentation Computer simulation Markov random fields Digital images |
| Content Type | Text |
| Resource Type | Article |
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