数值求解优化问题在活动轮廓模型上的应用

摘要

针对活动轮廓模型图像分割过程中迭代次数多,分割速度慢的问题,提出一种高阶的数值求解方法. 分析活动轮廓模型中基于全局信息的CV模型,以及基于局部信息的LBF模型,LIF模型. 使用二阶、三阶Runge-Kutta方法,原始Euler方法对模型进行数值求解实验对比分析. 并对LBF模型中平滑项系数,时间步长的选取进行讨论.通过对非同质图像、同质图像的实验结果分析表明,所采用的数值方法能够有效地提高数值收敛精度、减少迭代次数、计算效率高. 对不同系数和时间步长,数值方法也能表现出较好的稳定性.%In this paper , we analyze numerical optimization procedures and propose high-order numerical methods to deal with the problems of slow convergence and low efficiency in the active contour model .First, we analyze the global information region-based active contour Chan-Vese (CV) model, the local information region-based local bi-nary fitting ( LBF) model, and the local image fitting ( LIF) model.Then, we compare and analyze image segment results utilizing second-and third-order explicit Runge-Kutta methods , and the standard explicit Euler method .We also analyze the segment results of different sliding coefficient parameters and time steps of the LBF model .The ex-perimental results for the intensity inhomogeneities and common images show that the proposed numerical methods can reduce the number of iterations , and improve convergence accuracy and computational efficiency .In addition , for different coefficients and time steps , the proposed methods yield greater stability .