在Mum ford-Shah模型和Yoon Mo Jung等提出的分割模型的基础上,利用分段常数水平集方法,提出了一个新的多相位分割模型。新模型输出一个多相位的分布图象,并可以容易地提取每个单独的相位,有一定的抗噪音能力。利用最速下降算法求解总...在Mum ford-Shah模型和Yoon Mo Jung等提出的分割模型的基础上,利用分段常数水平集方法,提出了一个新的多相位分割模型。新模型输出一个多相位的分布图象,并可以容易地提取每个单独的相位,有一定的抗噪音能力。利用最速下降算法求解总变差最小化问题。引进了一个函数确定模型中的参数值,加速了算法的收敛速度。数值试验表明新模型有很好的分割效果且能准确地处理含T-交汇的图象。展开更多
In this work, we try to use the so-called Piecewise Constant Level Set Method （PCLSM） for the Mumford-Shah segmentation model. For image segmentation, the Mumford-Shah model needs to find the regions and the constan...In this work, we try to use the so-called Piecewise Constant Level Set Method （PCLSM） for the Mumford-Shah segmentation model. For image segmentation, the Mumford-Shah model needs to find the regions and the constant values inside the regions for the segmen- tation. In order to use PCLSM for this purpose, we need to solve a minimization problem using the level set function and the constant values as minimization variables. In this work, we test on a model such that we only need to minimize with respect to the level set function, i.e., we do not need to minimize with respect to the constant values. Gradient descent method and Newton method are used to solve the Euler-Lagrange equation for the minimization problem. Numerical experiments are given to show the efficiency and advantages of the new model and algorithms.展开更多
基金Supported by National Natural Science Committee and Chinese Engineering Physics Institute Foundation(10576013)Natural Science Foundation of Henan Province(0611053200)Natural Science Study Foundation of Henan University(06YBZR028)
文摘在Mum ford-Shah模型和Yoon Mo Jung等提出的分割模型的基础上,利用分段常数水平集方法,提出了一个新的多相位分割模型。新模型输出一个多相位的分布图象,并可以容易地提取每个单独的相位,有一定的抗噪音能力。利用最速下降算法求解总变差最小化问题。引进了一个函数确定模型中的参数值,加速了算法的收敛速度。数值试验表明新模型有很好的分割效果且能准确地处理含T-交汇的图象。
文摘In this work, we try to use the so-called Piecewise Constant Level Set Method （PCLSM） for the Mumford-Shah segmentation model. For image segmentation, the Mumford-Shah model needs to find the regions and the constant values inside the regions for the segmen- tation. In order to use PCLSM for this purpose, we need to solve a minimization problem using the level set function and the constant values as minimization variables. In this work, we test on a model such that we only need to minimize with respect to the level set function, i.e., we do not need to minimize with respect to the constant values. Gradient descent method and Newton method are used to solve the Euler-Lagrange equation for the minimization problem. Numerical experiments are given to show the efficiency and advantages of the new model and algorithms.
