Please use this identifier to cite or link to this item:
https://olympias.lib.uoi.gr/jspui/handle/123456789/10843Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Blekas, K. | en |
| dc.contributor.author | Likas, A. | en |
| dc.contributor.author | Galatsanos, N. P. | en |
| dc.contributor.author | Lagaris, I. E. | en |
| dc.date.accessioned | 2015-11-24T17:00:57Z | - |
| dc.date.available | 2015-11-24T17:00:57Z | - |
| dc.identifier.issn | 1045-9227 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/10843 | - |
| dc.rights | Default Licence | - |
| dc.subject | covex quadratic programming (qp) | en |
| dc.subject | expectation-maximization (em) | en |
| dc.subject | gaussian mixture model (gmm) | en |
| dc.subject | image segmentation | en |
| dc.subject | markov random field (mrf) | en |
| dc.subject | em algorithm | en |
| dc.title | A spatially constrained mixture model for image segmentation | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | Doi 10.1109/Tnn.2004.841773 | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.publicationDate | 2005 | - |
| heal.abstract | Gaussian mixture models (GMMs) constitute a well-known type of probabilistic neural networks. One of their many successful applications is in image segmentation, where spatially constrained mixture models have been trained using the expectation-maximization (EM) framework. In this letter, we elaborate on this method and propose a new methodology for the M-step of the EM algorithm that is based on a novel constrained optimization formulation. Numerical experiments using simulated images illustrate the superior performance of our method in terms of the attained maximum value of the objective function and segmentation accuracy compared to previous implementations of this approach. | en |
| heal.journalName | Ieee Transactions on Neural Networks | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Lagaris-2005-A spatially constrained mixture model for image segmentation.pdf | 339.02 kB | Adobe PDF | View/Open Request a copy |
This item is licensed under a Creative Commons License
