噪声符合高斯分布的曲面图像分割

针对噪声符合高斯分布的曲面图像分割问题,提出了一种基于变分水平集方法的隐式曲面上噪声符合高斯分布的图像分割模型。为定义隐式曲面上图像区域划分的动态轮廓线,引入另一个动态水平集函数,其零水平集与隐式曲面的交线即为曲面上区域划分的轮廓线。该模型采用N个水平集函数划分2^N个区域的区域竞争策略。通过变分方法得到图像分割空间轮廓线演化的梯度降方程。通过显式差分格式对演化方程进行离散。实验结果表明,该模型对曲面上噪声符合高斯分布的图像具有良好的分割效果,并且在图像分割的同时能够完全保持曲面固有的几何特征。

Gaussian Noise Image Segmentation on Implicit Surface

A new model to segment the Gaussian noise images on implicit surface based on variational level set method was proposed. The spatial contour for image segmentation is defined by means of intersection of the above implicit surface and the zero set of another dynamic level set function. Strategy for region competition is applied to segment regions using levelset function. The evolution equation of level set function is obtained using variation technique and is discretized by simple explicit finite difference method. Experimental results show that the model has promising effect on Gaussian noise image segmentation on implicit surface and preserves salient features of the surface.