A fast external force model for snake-based image segmentation

Snakes have been extensively used for object segmentation and tracking in computer vision and image processing applications. One of these drawbacks is that they converge slowly, since inverse matrix is computed at each iteration. We have introduced a new external force model, called mean-shift flow field (MSFF). This external force model is computed by a novel and fast mean-shift mask. This type of snake has the feature that it can enhance the convergence rates, and at the same time, maintain the properties of the traditional and GVF snakes. A conventional parametric snake model relies on some functions of the object contour gradient; in contrast the proposed method bases on MSFF which offers an automatic and definite termination criterion. To demonstrate the effectiveness of our algorithm, we present the processing results from synthetic and real images. The experimental results show that by incorporating mean-shift information into the snake framework, the new snake model provides excellent convergence speed and stability.