Curvelet-based geodesic snakes for image segmentation with multiple objects

Authors:
Hao Shan;Jianwei Ma
Affiliations:
School of Aerospace, Tsinghua University, Beijing 100084, China;School of Aerospace, Tsinghua University, Beijing 100084, China and Center of Geoscience, Ecole des Mines de Paris, 35 rue Saint-Honore, 77305 Fontainebleau, France
Venue:
Pattern Recognition Letters
Year:
2010

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Abstract

Curvelet transform is a multiscale and multidirectional geometric wavelet transform, which is an optimal sparse representation of edges and contours of objects. In this paper, a curvelet-based geodesic snake (CGS) is proposed for image segmentation of multiple objects. By producing the edge map of objects by curvelet thresholding instead of simple gradient methods, the proposed method shows great promises to recognize edges of multiple objects with weak edges and strong noises. In addition, we design several rules to quantitatively compare the segmentation accuracy.