@inproceedings{Zingman2015Detec-32958,
  year={2015},
  doi={10.1109/CVPRW.2015.7301387},
  title={Detection of incomplete enclosures of rectangular shape in remotely sensed images},
  isbn={978-1-4673-6760-8},
  publisher={IEEE},
  address={Piscataway},
  booktitle={2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) : Boston, Massachusetts, USA, 7 - 12 June 2015},
  pages={87--96},
  editor={IEEE},
  author={Zingman, Igor and Saupe, Dietmar and Lambers, Karsten}
}

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    <dc:creator>Saupe, Dietmar</dc:creator>
    <dcterms:title>Detection of incomplete enclosures of rectangular shape in remotely sensed images</dcterms:title>
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    <dc:creator>Zingman, Igor</dc:creator>
    <dc:contributor>Lambers, Karsten</dc:contributor>
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    <dcterms:abstract xml:lang="eng">We develop an approach for detection of ruins of livestock enclosures in alpine areas captured by high-resolution remotely sensed images. These structures are usually of approximately rectangular shape and appear in images as faint fragmented contours in complex background. We address this problem by introducing a new rectangularity feature that quantifies the degree of alignment of an optimal subset of extracted linear segments with a contour of rectangular shape. The rectangularity feature has high values not only for perfect enclosures, but also for broken ones with distorted angles, fragmented walls, or even a completely missing wall. However, it has zero value for spurious structures with less than three sides of a perceivable rectangle. Performance analysis using large imagery of an alpine environment is provided. We show how the detection performance can be improved by learning from only a few representative examples and a large number of negatives.</dcterms:abstract>
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    <dcterms:issued>2015</dcterms:issued>
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