| Paper: | MA-L2.2 |
| Session: | Image And Video Segmentation I |
| Time: | Monday, September 17, 10:10 - 10:30 |
| Presentation: |
Lecture
|
| Title: |
A VARIATIONAL FRAMEWORK FOR PARTIALLY OCCLUDED IMAGE SEGMENTATION USING COARSE TO FINE SHAPE ALIGNMENT AND SEMI-PARAMETRIC DENSITY APPROXIMATION |
| Authors: |
Lin Yang; Rutgers University | | |
| | David Foran; The Cancer Institute of New Jersey, UMDNJ | | |
| Abstract: |
In this paper, we propose a variational framework which combines top-down and bottom-up information to address the challenge of partially occluded image segmentation. The algorithm applies shape priors and divides shape learning into shape mode clustering and non-rigid transformation estimation to handle intraclass and interclass coarse to fine variations. A semi-parametric density approximation using adaptive meanshift and L2E robust estimation is used to model the likelihood. A set of real images is used to show the good performance of the algorithm. |
