Bayesian Approaches to Image Segmentation
When segmenting their environment into meaningful regions, human observers
exploit a number of low-level cues (such as intensity, color, texture or
motion information) and higher level knowledge about objects of interest.
In my presentation, I will present ways to incorporate such information
into image segmentation methods. In particular, I will present:
- the 'Diffusion Snake' as a fast spline-based implementation of the
- 'Motion Competition' as an extension of the Mumford-Shah framework from
intensity segmentation to motion segmentation. Segmenting contours are
represented either by splines or by level sets.
- the integration of higher-level statistical shape priors into the
segmentation processes. This permits to cope with noise, background
clutter and partial occlusions of the objects of interest.