摘要
针对传统几何活动轮廓(GAC)模型不能实现自适应分割,且容易出现边界泄漏和演化时间较长的缺点,提出了一个基于GAC与分形盒维数的图像分割算法。该算法结合了图像信息(图像分形盒维数)和演化曲线的位置,用与演化曲线内外区域分盒维数相关的演化速度v(D)代替传统GAC模型中的常量速度v。实验结果表明该算法可以使演化曲线根据其位置自适应地向内或者向外运动,减少了分割时间,并且在一定程度上减少了边界泄漏。
An image segmentation algorithm based on Geometric Active Contour（GAC） model and fractional box-counting dimension is presented in order to solve the problem that tradition GAC model can＇t segment adaptively and this kind of model usually leaks boundary and needs long time＇s evolution.This algorithm combines image information （fractional box-counting dimension of image） and curve location.In this algorithm, evolution speed v（D） which depends on fractional box-counting dimension of inward and outward area＇s of curve replaced constant speed v in tradition GAC model.Experimental results show that the proposed algorithm is feasible, reduces segmentation time and reaches an obvious effect in terms of boundary leaking.
出处
《计算机工程与应用》
CSCD
北大核心
2010年第27期191-194,共4页
Computer Engineering and Applications
关键词
几何活动轮廓模型
图像分割
分形盒维数
边界泄漏
geometric active contour model
image segmentation
fractional box-counting dimension
boundary leaking
