The Voronoi diagram-based dual-front scheme is known as a powerful and efficient technique for addressing the image segmentation and domain partitioning problems. In the basic formulation of existing dual-front approaches, the evolving contour can be considered as the interfaces of adjacent Voronoi regions. Among these dual-front models, a crucial ingredient is regarded as the geodesic metrics by which the geodesic distances and the corresponding Voronoi diagram can be estimated. In this paper, we introduce a new dual-front model based on asymmetric quadratic metrics. These metrics considered are built by the integration of the image features and a vector field derived from the evolving contour. The use of the asymmetry enhancement can reduce the risk for the segmentation contours being stuck at false positions, especially when the initial curves are far away from the target boundaries or the images have complicated intensity distributions. Moreover, the proposed dual-front model can be applied for image segmentation in conjunction with various region-based homogeneity terms. The numerical experiments on both synthetic and real images show that the proposed dual-front model indeed achieves encouraging results.

中文翻译：

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主动轮廓和图像分割的广义非对称双前线模型

基于Voronoi图的双前方案是解决图像分割和域划分问题的强大而有效的技术。在现有双前线方法的基本表述中，不断变化的轮廓可被视为相邻Voronoi区域的界面。在这些双前线模型中，关键要素被视为大地测量指标，据此可以估算出大地测量距离和相应的Voronoi图。在本文中，我们介绍了一种基于非对称二次度量的新型双前线模型。所考虑的这些度量是通过图像特征和从不断演变的轮廓得出的矢量场的集成而构建的。使用不对称增强功能可以减少分割轮廓卡在错误位置的风险，特别是当初始曲线远离目标边界或图像具有复杂的强度分布时。此外，所提出的双前模型可以结合各种基于区域的同质性术语应用于图像分割。在合成图像和真实图像上的数值实验表明，所提出的双前模型确实取得了令人鼓舞的结果。