计算机工程
計算機工程
계산궤공정
COMPUTER ENGINEERING
2014年
11期
220-224
,共5页
图像分割%测地活动轮廓模型%梯度向量场%追赶法%水平集方法%加性算子分裂算法
圖像分割%測地活動輪廓模型%梯度嚮量場%追趕法%水平集方法%加性算子分裂算法
도상분할%측지활동륜곽모형%제도향량장%추간법%수평집방법%가성산자분렬산법
image segmentation%Geodesic Active Contour ( GAC ) model%gradient vector flield%chasing method%level set method%Additive Operator Splitting( AOS) algorithm
基于偏微分方程的图像处理技术由于能够获得连续单像素的边缘而受到重视,其中梯度向量场与气球力混合作用的改进GAC模型———GAC_GVF&B克服了传统GAC模型的缺点,能准确地收敛到多目标图像和形状复杂图像的目标边界。但该算法的运行时间较长,影响算法的实际应用。为此,利用半隐式方案的加性算子分裂( AOS)算法,适当增大时间步长,降低迭代次数,对GAC_GVF&B模型的计算进行加速,在保证算法分割准确性的同时提高算法的收敛速度。实验结果表明,采用半隐式方案的AOS算法具有较好的图像分割效果,可有效减少所需的迭代次数,降低迭代时间和CPU运行时间,提高运行速率。
基于偏微分方程的圖像處理技術由于能夠穫得連續單像素的邊緣而受到重視,其中梯度嚮量場與氣毬力混閤作用的改進GAC模型———GAC_GVF&B剋服瞭傳統GAC模型的缺點,能準確地收斂到多目標圖像和形狀複雜圖像的目標邊界。但該算法的運行時間較長,影響算法的實際應用。為此,利用半隱式方案的加性算子分裂( AOS)算法,適噹增大時間步長,降低迭代次數,對GAC_GVF&B模型的計算進行加速,在保證算法分割準確性的同時提高算法的收斂速度。實驗結果錶明,採用半隱式方案的AOS算法具有較好的圖像分割效果,可有效減少所需的迭代次數,降低迭代時間和CPU運行時間,提高運行速率。
기우편미분방정적도상처리기술유우능구획득련속단상소적변연이수도중시,기중제도향량장여기구력혼합작용적개진GAC모형———GAC_GVF&B극복료전통GAC모형적결점,능준학지수렴도다목표도상화형상복잡도상적목표변계。단해산법적운행시간교장,영향산법적실제응용。위차,이용반은식방안적가성산자분렬( AOS)산법,괄당증대시간보장,강저질대차수,대GAC_GVF&B모형적계산진행가속,재보증산법분할준학성적동시제고산법적수렴속도。실험결과표명,채용반은식방안적AOS산법구유교호적도상분할효과,가유효감소소수적질대차수,강저질대시간화CPU운행시간,제고운행속솔。
The Partial Differential Equation ( PDE ) image processing is an advanced technology due to the ability of obtaining continuous and one-pixel edges. The improved Geodesic Active Contour( GAC) model by using the gradient vector field and a balloon force———GAC_GVF&B is an important one,because it can convergence accurately to target edges on images with multi-object or complex objects. But the model suffers from long running time which blocks its application. By appropriately increasing the time step and reducing the number of iterations,a semi-implicit additive split operator———Additive Operator Splitting ( AOS ) is used to speed up the computing of the GAC_GVF&B model and improve the convergence rate with the same accuracy of the segmentation. Experimental results show that the AOS algorithm is correct and effective,can reduces the number of iterations required,and lowers iteration time and cpu time. Furthermore,it speeds up the segmentation.