Abstract
Image segmentation is an important research area in Computer Vision and the GVF-snake is an effective segmentation algorithm presented in recent years. Traditional GVF-snake algorithm has a large capture range and can deal with boundary concavities. However, when interesting object has deep concavities, traditional GVF-snake algorithm can’t converge to true boundaries exactly. In this paper, a novel improved scheme was proposed based on the GVF-snake. The central idea is introduce dynamic balloon force and tangential force to strengthen the static GVF force. Experimental results of synthetic image and real image all demonstrated that the improved algorithm can capture boundary concavities better and detect complex edges more accurately.
Highlights
IntroductionA tracking method using active curves or surfaces, has been proposed in recent years
Active contours model, a tracking method using active curves or surfaces, has been proposed in recent years
The GVF-snake can’t converge to deep boundary concavities accurately, so we propose to design dynamic balloon force and tangential force to strengthen the GVF force to improve the capture ability of desired edges
Summary
A tracking method using active curves or surfaces, has been proposed in recent years It can integrate edge and region information and has been widely used in image segmentation field. Cohen et al has proposed the balloon snake [6] and the distance snake [7] Both of them increase the capture range of external force field and make the placing of initial curve easy. Xu et al has introduced the GVF-snake which enlarges capture range and can deal with boundary concavities [8]. The GVF-snake has large capture range and considerable ability to handle boundary concavities, it is difficult to realize accurate segmentation when detecting complex shape object with deep concavities. We introduce dynamic balloon force and tangential force to strengthen the static GVF force, and it is found that the capability of capturing concave edges enhanced effectively
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