Michailovich Oleg, Tannenbaum Allen
Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
IEEE Trans Image Process. 2008 Dec;17(12):2403-12. doi: 10.1109/TIP.2008.2006455.
The problem of segmentation of tracking sequences is of central importance in a multitude of applications. In the current paper, a different approach to the problem is discussed. Specifically, the proposed segmentation algorithm is implemented in conjunction with estimation of the dynamic parameters of moving objects represented by the tracking sequence. While the information on objects' motion allows one to transfer some valuable segmentation priors along the tracking sequence, the segmentation allows substantially reducing the complexity of motion estimation, thereby facilitating the computation. Thus, in the proposed methodology, the processes of segmentation and motion estimation work simultaneously, in a sort of "collaborative" manner. The Bayesian estimation framework is used here to perform the segmentation, while Kalman filtering is used to estimate the motion and to convey useful segmentation information along the image sequence. The proposed method is demonstrated on a number of both computed-simulated and real-life examples, and the obtained results indicate its advantages over some alternative approaches.
在众多应用中，跟踪序列的分割问题至关重要。在当前论文中，讨论了针对该问题的一种不同方法。具体而言，所提出的分割算法是结合对由跟踪序列表示的运动对象的动态参数估计来实现的。虽然关于对象运动的信息允许人们沿着跟踪序列传递一些有价值的分割先验知识，但分割能够大幅降低运动估计的复杂度，从而便于计算。因此，在所提出的方法中，分割和运动估计过程以一种“协作”的方式同时进行。这里使用贝叶斯估计框架来执行分割，而卡尔曼滤波用于估计运动并沿着图像序列传递有用的分割信息。在所提出的方法在一些计算机模拟和实际例子上得到了验证，并且所获得的结果表明了它相对于一些替代方法的优势。
