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, et al....

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Bibliographic Details
Published in2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) Vol. 1; pp. 474 - 479 vol. 1
Main Authors Guo Cao, Xin Yang, Zhihong Mao
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Discrete cosine transforms
Fractals
Hidden Markov models
Image segmentation
Layout
Level set
Object detection
Parameter estimation
Pattern recognition
Roads
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Summary: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.
ISBN:0769523722
9780769523729
ISSN:1063-6919
DOI:10.1109/CVPR.2005.52