数值求解优化问题在活动轮廓模型上的应用

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。

Application of a numerical solution to the optimization problem in the active contour model

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 binary 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 experimental 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.