.png){kind=icon}
返回
Global Intensity Correction in Dynamic Scenes
.png){kind=icon}
OA
AI
DOI：10.1007/s11263-009-0247-8.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Changing image intensities causes problems for many computer vision applications operating in unconstrained environments. We propose generally applicable algorithms to correct for global differences in intensity between images recorded with a static or slowly moving camera, regardless of the cause of intensity variation. The proposed intensity correction is based on intensity-quotient estimation. Various intensity estimation methods are compared. Usability is evaluated with background classification as example application. For this application we introduced the PIPE error measure evaluating performance and robustness to parameter setting. Our approach retains local intensity information, is always operational and can cope with fast changes in intensity. We show that for intensity estimation, robustness to outliers is essential for dynamic scenes. For image sequences with changing intensity, the best performing algorithm (MofQ) improves foreground-background classification results up to a factor two to four on real data.
Keyword:
Intensity correction
Intensity variation
Camera calibration
Pixel classification
Time-varying imagery
期刊
IF:
9.3
论文数:
3.9K
被引数:
2.8W
机构
引用论文
分享收藏Shadow elimination for effective moving object detection by Gaussian shadow modeling通过高斯阴影建模消除阴影，实现有效的运动目标检测
PREAI
分享收藏The anticoagulation factor protein S and its relative, Gas6, are ligands for the Tyro 3/Axl family of receptor tyrosine kinases.png)
Cell
OAAI
CellIF0
OAAI分享收藏
查看更多内容