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SEGMENTATION AND APPEARANCE MODEL BUILDING FROM AN IMAGE SEQUENCE

机译:图像序列分割和外观模型建设

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In this paper we explore the problem of accurately segmenting a person from a video given only approximate location of that person. Unlike previous work which assumes that the appearance model is known in advance, we developed an iterative expectation-sampling (ES) algorithm for solving segmentation and appearance modeling simultaneously. The appearance model is encoded with a kernel-based PDF defined in a joint color/path-length space. This appearance model remains unchanged during a short time period, although the object can articulate. Thus, we can perform the ES iteration not only for a single frame but also for an image sequence. The algorithm is iterative, but simple, efficient and gives visually good results.
机译:在本文中,我们探讨了从视频的近似位置准确地分割一个人的问题。与先前的工作不同,假设外观模型事先已知出现外观模型,我们开发了一种迭代期望 - 采样(ES)算法,用于同时解决分割和外观建模。外观模型用基于内核的PDF编码,在关节颜色/路径长度空间中定义。虽然物体可以表达,但在短时间内,这种外观模型保持不变。因此,我们不仅可以针对单个帧来执行eS迭代,而且还可以执行图像序列。该算法是迭代的,但简单,高效,并提供视觉上的结果。

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