Cast Shadow Removal Combining Local and Global Features
文献类型：会议论文
| 作者 | Liu Zhou; Kaiqi Huang ; Tieniu Tan
|
| 出版日期 | 2007 |
| 会议日期 | 2007-06-01 |
| 会议地点 | Minneapolis, Minnesota, USA |
| 关键词 | Local And Global Features |
| 页码 | 1-8 |
| 英文摘要 | 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. |
| 会议录 | CVPR workshop on the Seventh International Workshop on Visual Surveillance
 |
| 语种 | 英语 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12724]  |
| 专题 | 自动化研究所_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]. 见:. Minneapolis, Minnesota, USA. 2007-06-01. |
入库方式： OAI收割
来源：自动化研究所
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