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

Authors:
Guo Cao;Xin Yang;Zhihong Mao
Affiliations:
Shanghai Jiao Tong University;Shanghai Jiao Tong University;Shanghai Jiao Tong University
Venue:
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Year:
2005

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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[1], et al. 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.