一种改进的活动轮廓图像分割技术

Image Segmentation with an Improved Active Contour

图像分割是由图像处理到图像分析的关键步骤 ,也是一种基本的计算机视觉技术。针对传统的活动轮廓外力模型均存在一些难以克服的缺点 ,提出了一种改进的活动轮廓图像分割技术 ,并首先介绍了用活动轮廓进行目标分割的基本原理 ,即一条曲线在其内部能量和外部能量的共同作用下 ,可以移动到所期望的位置 ,并且当曲线到达目标位置的时候 ,活动曲线所具有的能量达到最小。在传统的活动轮廓中 ,外部能量通常由目标点的梯度势能场给出 ,但是由于梯度势能场存在着一些难以克服的缺点 ,即不能够很好地指导曲线的移动 ,为此 ,对其进行了改进 ,即采用一种梯度向量流场作为外部能量场的方法 ,从而有效地克服了传统梯度势能场捕捉范围小以及难以处理凹平面的缺点 ,并通过实验证明了该方法的有效性。

Image segmentation is a key process from image processing to image analysis, which is also a basic technique in Computer Vision. In this paper the authors first introduce the theory of the active contour. The active contour is something different from the common segmentation method. During the processing, the active contour finds the optimal value for every pixel in a small domain but also considers the relationship between different pixels as well. And as a result, the active contour can give out a smooth and continuous contour of the aim object. The basic idea of the active contour is to make the contour move to the destination with the internal and external energy. When the contour moves to the target point the total energy of the contour becomes minimum. Traditionally the external force of the active contour is given by the gradient potential energy, which has some insurmountable shortcomings, thus it cannot direct the contour to move to the destination correctly. Due to the character of the edge, the diffuse method is applied to the gradient of the edge i.e. the gradient vector flow (GVF). The GVF field maintains the merit of the gradient in the range nearby the edge but also diffuses the energy field to the slowly changed range as well, where the traditional gradient energy is very little. The GVF not only expands the effective range of the energy field but also enhances the ability to deal with the concave surface as well. The experimental results show the effectivity of the method.