ML Anthology

A Two-Stage Level Set Evolution Scheme for Man-Made Objects Detection in Aerial Images

Guo Cao, Xin Yang, Zhihong Mao
CVPR 2005 pp. 474-479
doi:10.1109/CVPR.2005.52 /cvpr/2005/cao2005cvpr-two/

Abstract

A novel two-stage level set evolution method for detecting man-made objects in aerial images is described. The method is based on a modified Mumford-Shah model and it uses a two-stage curve evolution strategy to get a preferable detection. It applies fractal error metric, developed by Cooper, et al. (1994) at the first curve evolution stage and adds additional constraint texture edge descriptor that is defined by using DCT (discrete cosine transform) coefficients on the image at the next stage. Man-made objects and natural areas are optimally differentiated by evolving the partial differential equation. The method artfully avoids selecting a threshold to separate the fractal error image, while an improper threshold often results in great segmentation errors. Experiments of the segmentation show that the proposed method is efficient.

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Cite

Text

Cao et al. "A Two-Stage Level Set Evolution Scheme for Man-Made Objects Detection in Aerial Images." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005. doi:10.1109/CVPR.2005.52

Markdown

[Cao et al. "A Two-Stage Level Set Evolution Scheme for Man-Made Objects Detection in Aerial Images." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005.](https://mlanthology.org/cvpr/2005/cao2005cvpr-two/) doi:10.1109/CVPR.2005.52

BibTeX

@inproceedings{cao2005cvpr-two,
  title     = {{A Two-Stage Level Set Evolution Scheme for Man-Made Objects Detection in Aerial Images}},
  author    = {Cao, Guo and Yang, Xin and Mao, Zhihong},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2005},
  pages     = {474-479},
  doi       = {10.1109/CVPR.2005.52},
  url       = {https://mlanthology.org/cvpr/2005/cao2005cvpr-two/}
}