Pixon-based image segmentation with Markov random fields
Image segmentation is an essential processing step for many image analysis applications. We propose a novel pixon-based adaptive scale method for image segmentation. The key idea of our approach is that a pixon-based image model is combined with a Markov random field (MRF) model under a Bayesian fra...
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 12(2003), 12 vom: 15., Seite 1552-9 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
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2003
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Schlagworte: | Journal Article |
| Zusammenfassung: | Image segmentation is an essential processing step for many image analysis applications. We propose a novel pixon-based adaptive scale method for image segmentation. The key idea of our approach is that a pixon-based image model is combined with a Markov random field (MRF) model under a Bayesian framework. We introduce a new pixon scheme that is more suitable for image segmentation than the "fuzzy" pixon scheme. The anisotropic diffusion equation is successfully used to form the pixons in our new pixon scheme. Experimental results demonstrate that our algorithm performs fairly well and computational costs decrease dramatically compared with the pixel-based MRF algorithm |
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| Beschreibung: | Date Completed 20.05.2010 Date Revised 04.02.2008 published: Print Citation Status PubMed-not-MEDLINE |
| ISSN: | 1057-7149 |
| DOI: | 10.1109/TIP.2003.817242 |