Geometric active contours without re-initialization for image segmentation

Authors:
Zheng Ying;Li Guangyao;Sun Xiehua;Zhou Xinmin
Affiliations:
College of Electronics and Information, Tongji University, Shanghai 201804, China;College of Electronics and Information, Tongji University, Shanghai 201804, China;College of Information Engineering, China Liliang University, Hangzhou 310018, China;College of Electronics and Information, Tongji University, Shanghai 201804, China
Venue:
Pattern Recognition
Year:
2009

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Abstract

A geometric active contour model without re-initialization that can be used for grey and color image segmentation is presented in this paper. It combines directional information about edge location based on Cumani operator as a part of driving force, with the improved geodesic active contours containing Bays error based statistical region information. Moreover, an extra term that penalizes the deviation of the level set function from a signed distance function is also included in the model, thus the costly re-initialization procedure can be completely eliminated and all these measures are integrated in a unified frame. Experimental results on real grey and color images have shown that our model can precisely extract contours of images and its performance is much better and faster than the geodesic-aided C-V (GACV) model.