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.
@article{arxiv.1307.3043,
title = {A two-layer Conditional Random Field for the classification of partially occluded objects},
author = {Sergey Kosov and Pushmeet Kohli and Franz Rottensteiner and Christian Heipke},
journal= {arXiv preprint arXiv:1307.3043},
year = {2013}
}