为提高几何活动轮廓分割算法的分割效率和准确性，设计了新的边缘检测与跟踪算法 . 首先采用矢量图像计算图像的梯度值，并设计能够自适应调整阈值的边缘指示函数，进而提出改进的变分水平集演化模型；然后设计基于该改进模型的边缘检测算法，并在无迹卡尔曼滤波器框架下设计了运动目标的跟踪算法 . 实验结果表明，文中算法不但显著地提高了轮廓演化模型的灵活性和收敛速度，而且对阴影、遮挡、目标形变和背景干扰等具有较好的鲁棒性 .

In order to improve the efficiency and accuracy of geometric active contour model-based segmentation algorithm, a novel edge detection and tracking algorithm is presented. Firstly, the gradient of an image is calculated according to vector image, and an edge indicator with adaptive threshold is proposed. Secondly, an improved evolution model using variational level set is put forward. Then, on the basis of this model, an improved edge detection algorithm is proposed, and a target tracking algorithm is designed in the framework of unscented Kalman filter. Experimental results demonstrate that the proposed algorithm not only increases the convergence rate and flexibility of active contour evolution model significantly but also possesses strong robustness to such interferences as shadow, occlusion, object deformation and background interference.