基于距离均衡化的自适应性动态轮廓模型

针对形变轮廓模型对初始位置敏感、易陷入局部极值以及不具备自动拓扑变换功能等问题，在讨论拓扑自适应的Snake模型的基础上，提出了基于距离均衡化的自适应性动态轮廓模型。该模型是首先通过顶点到其邻点连线的平均距离来改进内部能量项，使轮廓的运动更具稳定性，同时用轮廓自身的特性来决定轮廓的运动，使其具有较强的自适应性。然后通过膨胀力的构造和使用，使得该模型能够在较大范围内捕获图像的特征。用该方法对合成图像和真实图像进行的分割结果表明，效果较好。

On the Adaptive Active Contour Model Based on Distance Equalization

This paper, beginning with analyzing the characteristics of Topology adaptive Snake model, points out its weak points: sensitivity of initial position, tendency of the stability in a place, and failure of automatic Topology change. Therefore, this paper proposes a new model: the adaptive active contour model based on distance equalization. This new model, through the average distance between the vertex and the line of its neighborhood dots, improves the internal energy term in the Snake model, thus much more stabilizing the contour in the process of movement. Meanwhile, the characteristics of the contour itself determine the movement of the contour, so that the adaptability of such model has been comparatively improved. By constructing and applying the inflation force, this model could be of wide use in relatively bigger areas to capture the images. This paper also proves that the experiments of segmenting synthesis and real images, conducted under the guidance of the model, have achieved effective results.