Xu C, Prince J L
Dept. of Electr. and Comput. Eng., Johns Hopkins Univ., Baltimore, MD 21218, USA.
IEEE Trans Image Process. 1998;7(3):359-69. doi: 10.1109/83.661186.
Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Problems associated with initialization and poor convergence to boundary concavities, however, have limited their utility. This paper presents a new external force for active contours, largely solving both problems. This external force, which we call gradient vector flow (GVF), is computed as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the image. It differs fundamentally from traditional snake external forces in that it cannot be written as the negative gradient of a potential function, and the corresponding snake is formulated directly from a force balance condition rather than a variational formulation. Using several two-dimensional (2-D) examples and one three-dimensional (3-D) example, we show that GVF has a large capture range and is able to move snakes into boundary concavities.
蛇形模型，即活动轮廓模型，在计算机视觉和图像处理应用中被广泛使用，特别是用于定位物体边界。然而，与初始化相关的问题以及难以收敛到边界凹陷处的问题限制了它们的实用性。本文提出了一种新的活动轮廓外部力，很大程度上解决了这两个问题。这种外部力，我们称之为梯度向量流（GVF），是通过对从图像中导出的灰度或二值边缘图的梯度向量进行扩散来计算的。它与传统的蛇形模型外部力有根本区别，因为它不能写成势函数的负梯度，并且相应的蛇形模型是直接根据力平衡条件而不是变分公式来制定的。通过几个二维（2-D）示例和一个三维（3-D）示例，我们表明GVF具有较大的捕获范围，并且能够将蛇形模型移动到边界凹陷处。
