Please use this identifier to cite or link to this item:
https://olympias.lib.uoi.gr/jspui/handle/123456789/10970Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Blekas, K. | en |
| dc.contributor.author | Nikou, C. | en |
| dc.contributor.author | Galatsanos, N. | en |
| dc.contributor.author | Tsekos, N. V. | en |
| dc.date.accessioned | 2015-11-24T17:01:44Z | - |
| dc.date.available | 2015-11-24T17:01:44Z | - |
| dc.identifier.issn | 0218-2130 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/10970 | - |
| dc.rights | Default Licence | - |
| dc.subject | regression mixture model | en |
| dc.subject | curve clustering | en |
| dc.subject | expectation maximization | en |
| dc.subject | markov random field | en |
| dc.subject | smoothness prior | en |
| dc.subject | maximum-likelihood | en |
| dc.subject | image-restoration | en |
| dc.subject | contrast agent | en |
| dc.subject | em algorithm | en |
| dc.subject | segmentation | en |
| dc.title | A Regression Mixture Model with Spatial Constraints for Clustering Spatiotemporal Data | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.publicationDate | 2008 | - |
| heal.abstract | We present a new approach for curve clustering designed for analysis of spatiotemporal data. Such data contains both spatial and temporal patterns that we desire to capture. The proposed methodology is based on regression and Gaussian mixture modeling. The novelty of the here in work is the incorporation of spatial smoothness constraints in the form of a prior for the data labels. This allows to take into account the property of spatiotemporal data according to which spatially adjacent data points have higher probability to belong to the same cluster. The proposed model can be formulated as a Maximum a Posteriori (MAP) problem, where the Expectation Maximization (EM) algorithm is used to estimate the model parameters. Several numerical experiments with both simulated data and real cardiac perfusion MRI data are used for evaluating the methodology. The results are promising and demonstrate the value of the proposed approach | en |
| heal.journalName | International Journal on Artificial Intelligence Tools | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| blekas-2008-A regression mixture model with spatial constraints for clustering.pdf | 763.26 kB | Adobe PDF | View/Open Request a copy |
This item is licensed under a Creative Commons License
