| 题名: | Linearity of Each Channel Pixel Values from a Surface in and out of Shadows and Its Applications |
| 作者: | Tian JD(田建东) ; Tang YD(唐延东)
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| 作者部门: | 机器人学研究室
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| 会议名称: | IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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| 会议日期: | June 20-25, 2011
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| 会议地点: | Colorado Springs, CO
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| 会议主办者: | IEEE
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| 会议录: | 2011 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
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| 会议录出版者: | IEEE
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| 会议录出版地: | Piscataway, NJ, USA
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| 出版日期: | 2011
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| 页码: | 985-992
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| 收录类别: | EI
; CPCI（ISTP）
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| ISSN号: | 1063-6919
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| ISBN号: | 978-1-4577-0393-5
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| 关键词: | MOVING-OBJECTS
; CAST SHADOWS
; REMOVAL
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| 摘要: | Shadows, the common phenomena in most outdoor scenes, are illuminated by diffuse skylight whereas shaded from direct sunlight. Generally shadows take place in sunny weather when the spectral power distributions (SPD) of sunlight, skylight, and daylight show strong regularity: they principally vary with sun angles. In this paper, we first deduce that the pixel values of a surface illuminated by skylight (in shadow region) and by daylight (in non-shadow region) have a linear relationship, and the linearity is independent of surface reflectance and holds in each color channel. We then use six simulated images that contain 1995 surfaces and two real captured images to test the linearity. The results validate the linearity. Based on the deduced linear relationship, we develop three shadow processing applications include intrinsic image deriving, shadow verification, and shadow removal. The results of the applications demonstrate that the linear relationship have practical values. |
| 语种: | 英语
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| 产权排序: | 1
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| EI收录号: | 20113814347964
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| WOS记录号: | WOS:000295615801010
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| Citation statistics: |
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| 内容类型: | 会议论文
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| URI标识: | http://ir.sia.cn/handle/173321/8758
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| Appears in Collections: | 机器人学研究室_会议论文
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| HYQW000710.pdf（3117KB） | 会议论文 | -- | 开放获取 | | View
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| Recommended Citation: | |
Tian JD,Tang YD. Linearity of Each Channel Pixel Values from a Surface in and out of Shadows and Its Applications[C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Colorado Springs, CO. June 20-25, 2011.Linearity of Each Channel Pixel Values from a Surface in and out of Shadows and Its Applications.
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