Local regularity-driven city-scale facade detection from Aerial Images

Jingchen Liu, Yanxi Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

We propose a novel regularity-driven framework for facade detection from aerial images of urban scenes. Gini-index is used in our work to form an edge-based regularity metric relating regularity and distribution sparsity. Facade regions are chosen so that these local regularities are maximized. We apply a greedy adaptive region expansion procedure for facade region detection and growing, followed by integer quadratic programming for removing overlapping facades to optimize facade coverage. Our algorithm can handle images that have wide viewing angles and contain more than 200 facades per image. The experimental results on images from three different cities (NYC, Rome, San-Francisco) demonstrate superior performance on facade detection in both accuracy and speed over state of the art methods. We also show an application of our facade detection for effective cross-view facade matching.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PublisherIEEE Computer Society
Pages3778-3785
Number of pages8
ISBN (Electronic)9781479951178, 9781479951178
DOIs
StatePublished - Sep 24 2014
Event27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 - Columbus, United States
Duration: Jun 23 2014Jun 28 2014

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Other

Other27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014
CountryUnited States
CityColumbus
Period6/23/146/28/14

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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  • Cite this

    Liu, J., & Liu, Y. (2014). Local regularity-driven city-scale facade detection from Aerial Images. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 3778-3785). [6909878] (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). IEEE Computer Society. https://doi.org/10.1109/CVPR.2014.489