  Conditional Random Fields (CRF) are among the most popular techniques for
image labelling because of their flexibility in modelling dependencies between
the labels and the image features. This paper proposes a novel CRF-framework
for image labeling problems which is capable to classify partially occluded
objects. Our approach is evaluated on aerial near-vertical images as well as on
urban street-view images and compared with another methods.
