18. A. Pinz: Categorization-Driven Segmentation: Active Shape Models for Categories
The localization of objects in images is still a field of
current categorization research.
It would be desirable to obtain improved object segmentation for existing categorization
approaches, in which category recognition and approximate object delineation
may
already be satisfactoy. We build on our previous results in category detection
using a
Boundary-Fragment-Model (BFM, Opelt et al. ECCV06, CVPR06) and in segmentation
of specific
objects using shape priors in a level set framework (Fussenegger et al. ACCV06).
Our novel
segmentation approach starts by training standard BFMs for a number of categories.
In the recognition phase, these BFMs successfuly localize instances of the
learnt categories
in test images. The result is a collection of boundary fragments that vote
for an object
centroid. In the final segmentation phase, we use these boundary fragments,
smooth them,
calculate a gradient vector flow field and use it as a generic shape prior
for a level set
based segmentation of the test image. First experiments (Fussenegger et al.
ICPR06) show
very accurate segmentation results for bottles, cars side and cows side. Further
results for
our multiclass dataset are presented in this poster. Potential future extensions
include
incremental, online learning of generic shape priors and handling of significant
amount
of occlusion.