A MRF Shape Prior for Facade Parsing With Occlusions

Mateusz Kozinski, Raghudeep Gadde, Sergey Zagoruyko, Guillaume Obozinski, Renaud Marlet; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 2820-2828

Abstract


We present a new shape prior formalism for segmentation of rectified facade images. It combines the simplicity of split grammars with unprecedented expressive power: the capability of encoding simultaneous alignment in two dimensions, facade occlusions and irregular boundaries between facade elements. Our method simultaneously segments the visible and occluding objects and recovers the structure of the occluded facade. We formulate the task of finding the most likely image segmentation conforming to a prior of the proposed form as a MAP-MRF problem over the standard 4-connected pixel grid with hard constraints on the classes of neighboring pixels, and propose an efficient optimization algorithm for solving it. We demonstrate state of the art results on a number of facade segmentation datasets.

Related Material


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[bibtex]
@InProceedings{Kozinski_2015_CVPR,
author = {Kozinski, Mateusz and Gadde, Raghudeep and Zagoruyko, Sergey and Obozinski, Guillaume and Marlet, Renaud},
title = {A MRF Shape Prior for Facade Parsing With Occlusions},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}