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https://olympias.lib.uoi.gr/jspui/handle/123456789/11046Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Sfikas, G. | en |
| dc.contributor.author | Nikou, C. | en |
| dc.contributor.author | Galatsanos, N. | en |
| dc.contributor.author | Heinrich, C. | en |
| dc.date.accessioned | 2015-11-24T17:02:22Z | - |
| dc.date.available | 2015-11-24T17:02:22Z | - |
| dc.identifier.issn | 0924-9907 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/11046 | - |
| dc.rights | Default Licence | - |
| dc.subject | image segmentation | en |
| dc.subject | spatially varying gaussian mixture model | en |
| dc.subject | gauss-markov random field | en |
| dc.subject | maximum a posteriori (map) estimation | en |
| dc.subject | edge-preservation | en |
| dc.subject | expectation-maximization (em) | en |
| dc.subject | variational inference | en |
| dc.subject | measure field models | en |
| dc.subject | markov random-fields | en |
| dc.subject | expectation-maximization | en |
| dc.subject | restoration | en |
| dc.subject | algorithms | en |
| dc.subject | cuts | en |
| dc.title | Spatially Varying Mixtures Incorporating Line Processes for Image Segmentation | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | DOI 10.1007/s10851-009-0174-x | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.publicationDate | 2010 | - |
| heal.abstract | Spatially varying mixture models are characterized by the dependence of their mixing proportions on location (contextual mixing proportions) and they have been widely used in image segmentation. In this work, Gauss-Markov random field (MRF) priors are employed along with spatially varying mixture models to ensure the preservation of region boundaries in image segmentation. To preserve region boundaries, two distinct models for a line process involved in the MRF prior are proposed. The first model considers edge preservation by imposing a Bernoulli prior on the normally distributed local differences of the contextual mixing proportions. It is a discrete line process model whose parameters are computed by variational inference. The second model imposes Gamma prior on the Student's-t distributed local differences of the contextual mixing proportions. It is a continuous line process whose parameters are also automatically estimated by the Expectation-Maximization (EM) algorithm. The proposed models are numerically evaluated and two important issues in image segmentation by mixture models are also investigated and discussed: the constraints to be imposed on the contextual mixing proportions to be probability vectors and the MRF optimization strategy in the frameworks of the standard and variational EM algorithm. | en |
| heal.journalName | Journal of Mathematical Imaging and Vision | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Sfikas-2010-Spatially Varying Mi.pdf | 1.37 MB | Adobe PDF | View/Open Request a copy |
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