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Mumford-Shah Model Based Man-Made Objects Detection from Aerial Images

  • Guo Cao
  • Xin Yang
  • Dake Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3459)

Abstract

In this paper, a novel method for detecting man-made objects in aerial images is described. The method is based on a simplified Mumford-Shah model. It applies fractal error metric, developed by Cooper, et al [1] and additional constraint, a texture edge descriptor which is defined by DCT (Discrete Cosine Transform) coefficients on the image, to get a preferable segmentation. Man-made objects and natural areas are optimally differentiated by evolving the partial differential equation using this Mumford-Shah model. The method artfully avoids selecting a threshold to separate the fractal error image, since an improper threshold may result large segmentation errors. Experiments of the segmentation show that the proposed method is efficient.

Keywords

Discrete Cosine Transform Synthetic Aperture Radar Image Natural Scene Aerial Image Fractal Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Guo Cao
    • 1
  • Xin Yang
    • 1
  • Dake Zhou
    • 1
  1. 1.Institute of Image Processing and Pattern RecognitionShanghai Jiao Tong UniversityShanghaiP. R. China

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