R-snakes

Zagorchev, Lyubomir; Satter, Martin

doi:10.1016/j.imavis.2006.07.008

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“…Sun et al introduced adaptive filters to T-snake so that it can deal with several interlaced objects [4]. Zagorchev et al proposed R-snake based on rotating Gaussian curve [5]. R-snake is capable of changing the curve's rigidity, so object's shape could be reconstructed smoothly.…”

Section: Introductionmentioning

confidence: 99%

An Improved Algorithm Based on the GVF-Snake for Effective Concavity Edge Detection

Zhang¹,

Li²,

Li³

et al. 2013

JSEA

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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.

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Section: Introductionmentioning

confidence: 99%