Mumford-Shah model based man-made objects detection from aerial images

Authors:
Guo Cao;Xin Yang;Dake Zhou
Affiliations:
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, P. R. China;Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, P. R. China;Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, P. R. China
Venue:
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
Year:
2005

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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.