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Cast Shadow Removal Combining Local and Global Features
Liu Zhou; Kaiqi Huang; Tieniu Tan
2007
会议名称CVPR workshop on the Seventh International Workshop on Visual Surveillance
会议录名称CVPR workshop on the Seventh International Workshop on Visual Surveillance
页码1-8
会议日期2007-06-01
会议地点Minneapolis, Minnesota, USA
摘要In this paper, we present a method using pixel-level information, local region-level information and global-level information to remove shadow. At the pixel-level, we employ GMM to model the behavior of cast shadow for every pixel in the HSV color space, as it can deal with complex illumination conditions. However, unlike the GMM for background which can obtain sample every frame, this model for shadow needs more frames to get the same number of sample, because shadow may not appear at the same pixel for each frame. Therefore, it will take a long time to converge. To overcome this drawback, we use the local region-level information to get more samples and global-level information to improve a preclassifier and then, by using it, we get samples which are more likely to be shadow. Also, at the local region-level, we use Markov random fields to represent dependencies between the label of single pixel and labels of its neighborhood. Moreover, to make global level information more robust, tracking information is used. Experimental results show that the proposed method is efficient and robust.
关键词Local And Global Features
语种英语
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/12724
专题智能感知与计算研究中心
通讯作者Kaiqi Huang
作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Liu Zhou,Kaiqi Huang,Tieniu Tan. Cast Shadow Removal Combining Local and Global Features[C],2007:1-8.
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