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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Li Qingsheng Liu Guoying |
| Copyright Year | 2010 |
| Description | Author affiliation: School of Computer and Information Engineering, Anyang Normal University, Henan, China (Li Qingsheng; Liu Guoying) |
| Abstract | The traditional multi-resolution Markov random field (MRMRF) model uses two-component Markov random field model on each resolution, and requires training data to estimate the necessary model parameters, which is unsuitable for unsupervised image segmentation. Under this circumstance, a new multi-resolution Markov random field model with variable potential for unsupervised texture image segmentation is presented. The new model solves this problem by introducing a variable potential function for multi-level logistic distribution (MLL) model on each scale. Using this method, the new model can automatically estimate model parameters and produce accurate unsupervised segmentation results. The results obtained on synthetic texture images and remote sensing images demonstrate that a better segmentation is achieved by our model than the traditional MRMRF model. |
| File Size | 644113 |
| File Format | |
| ISBN | 9781424472352 |
| e-ISBN | 9781424472376 |
| DOI | 10.1109/ICCASM.2010.5623020 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-10-22 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image segmentation Image resolution Estimation Variable potential Argon Remote sensing Multiresolution Markov Random Field |
| Content Type | Text |
| Resource Type | Article |
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