论文标题
基于隐式区域的同质性增强的图像分割的大地测量路径
Geodesic Paths for Image Segmentation with Implicit Region-based Homogeneity Enhancement
论文作者
论文摘要
由于其全局最优性和良好的数值解决方案（例如快速行进方法），最小路径被认为是边界检测和图像分割的强大而有效的工具。在本文中，我们基于基于区域的均匀性增强框架，基于Eikonal部分微分方程（PDE）框架引入了灵活的交互式图像分割模型。引入模型中的一个关键成分是构建局部测量指标，它们能够整合各向异性和不对称边缘特征，基于隐式区域的同质性特征和/或曲率正则化。将基于区域的同质性特征纳入所考虑的指标依赖于这些特征的隐性表示，这是这项工作的贡献之一。此外，我们还引入了一种构建简单封闭轮廓的方法，作为两个不相交曲线的串联。实验结果证明，所提出的模型确实优于最小的基于路径的最小图像分割方法。
Minimal paths are regarded as a powerful and efficient tool for boundary detection and image segmentation due to its global optimality and the well-established numerical solutions such as fast marching method. In this paper, we introduce a flexible interactive image segmentation model based on the Eikonal partial differential equation (PDE) framework in conjunction with region-based homogeneity enhancement. A key ingredient in the introduced model is the construction of local geodesic metrics, which are capable of integrating anisotropic and asymmetric edge features, implicit region-based homogeneity features and/or curvature regularization. The incorporation of the region-based homogeneity features into the metrics considered relies on an implicit representation of these features, which is one of the contributions of this work. Moreover, we also introduce a way to build simple closed contours as the concatenation of two disjoint open curves. Experimental results prove that the proposed model indeed outperforms state-of-the-art minimal paths-based image segmentation approaches.
