Tang Xiangyu, von der Malsburg Christoph
Computer Science Department, University of Southern California, Los Angeles, CA, 90089, U.S.A.
Neural Comput. 2008 Jun;20(6):1452-72. doi: 10.1162/neco.2008.03-06-176.
This letter presents an improved cue integration approach to reliably separate coherent moving objects from their background scene in video sequences. The proposed method uses a probabilistic framework to unify bottom-up and top-down cues in a parallel, "democratic" fashion. The algorithm makes use of a modified Bayes rule where each pixel's posterior probabilities of figure or ground layer assignment are derived from likelihood models of three bottom-up cues and a prior model provided by a top-down cue. Each cue is treated as independent evidence for figure-ground separation. They compete with and complement each other dynamically by adjusting relative weights from frame to frame according to cue quality measured against the overall integration. At the same time, the likelihood or prior models of individual cues adapt toward the integrated result. These mechanisms enable the system to organize under the influence of visual scene structure without manual intervention. A novel contribution here is the incorporation of a top-down cue. It improves the system's robustness and accuracy and helps handle difficult and ambiguous situations, such as abrupt lighting changes or occlusion among multiple objects. Results on various video sequences are demonstrated and discussed. (Video demos are available at http://organic.usc.edu:8376/ approximately tangx/neco/index.html .).
本文提出了一种改进的线索整合方法，用于在视频序列中可靠地将连贯的运动物体与其背景场景分离。所提出的方法使用概率框架，以并行的“民主”方式统一自下而上和自上而下的线索。该算法利用修改后的贝叶斯规则，其中每个像素的前景或背景层分配的后验概率来自三个自下而上线索的似然模型和一个自上而下线索提供的先验模型。每个线索都被视为用于前景-背景分离的独立证据。它们通过根据相对于整体整合测量的线索质量逐帧调整相对权重，动态地相互竞争和补充。同时，各个线索的似然或先验模型向整合结果自适应。这些机制使系统能够在视觉场景结构的影响下进行组织，无需人工干预。这里的一个新颖贡献是纳入了一个自上而下的线索。它提高了系统的鲁棒性和准确性，并有助于处理困难和模糊的情况，例如突然的光照变化或多个物体之间的遮挡。展示并讨论了在各种视频序列上的结果。（视频演示可在http://organic.usc.edu:8376/ approximately tangx/neco/index.html 获得。）
