| Paper: | MA-P2.9 |
| Session: | Active-Contour, Level-Set, and Cluster-Based Segmentation Methods |
| Time: | Monday, September 17, 09:50 - 12:30 |
| Presentation: |
Poster
|
| Title: |
ROBUST IMAGE SEGMENTATION WITH MIXTURES OF STUDENT T-DISTRIBUTIONS |
| Authors: |
Giorgos Sfikas; University of Ioannina | | |
| | Christophoros Nikou; University of Ioannina | | |
| | Nikolaos Galatsanos; University of Ioannina | | |
| Abstract: |
Gaussian mixture models have been widely used in image segmentation. However, such models are sensitive to outliers. In this paper, we consider a robust model for image segmentation based on mixtures of Student t-distributions which have heavier tails than Gaussian and thus are not sensitive to outliers. The t-distribution is one of the few heavy tailed probability density functions (pdf) closely related to the Gaussian, that gives tractable maximum likelihood inference via the Expectation-Maximization (EM) algorithm. Numerical experiments that demonstrate the properties of the proposed model for image segmentation are presented. |
