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Object Segmentation Based on a Nonparametric Snake with Motion Prediction in Video
Abstract
Object segmentation in video sequence is a basic and important task in video applications such as surveillance systems and video coding. Nonparametric snake algorithms for object segmentation have been proposed to overcome the drawback of conventional snake algorithms: the dependency on several parameters. In this chapter, a new object segmentation algorithm for video, based on a nonparametric snake model with motion prediction, is proposed. Object contour is initialized by using the mean absolute difference of intensity between input and previous frames. And in order to convert initial object contours into more exact object contours, the gradient vector flow snake is used. Finally object contour is predicted using a Kalman filter in successive frames. The proposed object segmentation method for video can provide more detailed and improved object segmentation results than the conventional methods. Various experimental results show the effectiveness of the proposed method in terms of the pixel-based quality measure and the computation time.
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