区域配对引导的光照传播视频阴影去除方法

传统方法在处理自由移动相机捕获视频中的阴影时,存在时空不连贯现象。为解决该问题,提出一种区域配对引导的光照传播阴影去除方法。首先,使用基于尺度不变特征变换(SIFT)特征向量的均值漂移方法分割视频,通过支持向量机(SVM)分类器检测出其中的阴影;然后,将输入视频帧分解成重叠的二维图像区域块,建立其马尔可夫随机场(MRF),通过光流引导的区域块匹配机制,为每一个阴影块找到最佳匹配的非阴影块;最后,使用局部光照传播算子恢复阴影区域块的光照,并对其进行全局光照优化。实验结果表明,与传统基于光照传播方法相比,所提方法在阴影检测综合评价指标上提升约6. 23%,像素均方根误差(RMSE)减小约30. 12%,且大幅度缩短了阴影处理时间,得到的无阴影视频结果更具时空连贯性。

Video shadow removal method using region matching guided by illumination transfer

In order to solve spatio-temporally incoherent problem of traditional shadow removal methods for videos captured by free moving cameras, a shadow detection and removal approach using region matching guided by illumination transfer was proposed. Firstly, the input video was segmented by using Mean Shift method based on Scale Invariant Feature Transform (SIFT), and the video shadow was detected by Support Vector Machine (SVM) classifier. Secondly, the input video was decomposed into overlapped 2D patches, and a Markov Random Field (MRF) for this video was set up, and the corresponding lit patch for every shadow patch was found via region matching guided by optical flow. Finally, in order to get spatio-temporally coherent results, each shadow patch was processed with its matched lit patch by local illumination transfer operation and global shadow removal. The experimental results show that the proposed algorithm obtains higher accuracy and lower error than the traditional methods based on illumination transfer, the comprehensive evaluation metric is improved by about 6.23%, and the Root Mean Square Error (RMSE) is reduced by about 30.12%. It can obtain better shadow removal results with more spatio-temporal coherence but much less time.