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

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.

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Guo Cao, Xin Yang, Zhihong Mao
• Keywords: Computer Vision, Curve Evolution, CVPR 2005, Fractal Error, Two-stage Curve Evolution