This paper proposes an algorithm based on curve evolution for unsupervised texture segmentation.
提出了一种基于曲线演化的非监督式纹理分割算法。
In this thesis, we study mainly geometric curve evolution models and their application in medical images.
本文主要就几何曲线演化模型及其在医学图像中的应用进行探讨。
Level Set methods are robust and efficient numerical tools for resolving curve evolution in image segmentation.
水平集方法应用于图象分割的曲线或曲面进化问题，是一种稳定有效的数值计算方法。
In this paper, an efficient approach to ROI extraction based on monotonically marching curve evolution is proposed.
提出了一种基于单调推进曲线进化的感兴趣区域提取新方法。
Using gradient-descent methods the energy function is minimized and a curve evolution equation is obtained to segment the image.
运用梯度最陡下降法，推导出能量函数曲线演化方程，并应用于图像分割。
And then, the theory of curve evolution, how to solve the PDEs model based on level set method and its calculation methods are expatiated.
其次详细介绍了曲线演化理论、偏微分方程模型的水平集方法求解以及数值计算方法。
The Mumford-Shah model is more robust than curve evolution model to detect discontinuities under noisy environment, so it is more suitable for segmentation of noisy jacquard images.
提花织物图像分割是提花图案设计的关键，曲线演化模型是一种流行的图像分割方法，但是该方法无法检测含噪环境下的图像特征。
The Mumford-Shah model is more robust than curve evolution model to detect discontinuities under noisy environment, so it is more suitable for segmentation of noisy jacquard images.
提花织物图像分割是提花图案设计的关键，曲线演化模型是一种流行的图像分割方法，但是该方法无法检测含噪环境下的图像特征。
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