区域配对引导的光照传播视频阴影去除方法
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摘 要: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。
关键词:视频阴影;区域配对;光照传播;阴影去除;光流
中图分类号: TP391.41
文献标志码:A
Abstract: 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.
Key words: video shadow; region matching; illumination transfer; shadow removal; optical flow
0 引言
陰影去除在图像识别、光照估计、虚拟现实场景生成等领域均起到了至关重要的作用。受阴影检测识别复杂度的影响,视频分割与物体检测识别、图像/视频本征分解等领域算法的准确性和效率都会大大降低。此外,提取到的阴影可用于图像/视频场景编辑,从而生成更为生动的图像/视频效果。因此,视频阴影去除一直是视频处理领域的一个研究热点。
单幅图像阴影检测以及去除已经得到较为深入的研究[1-6]。文献[1]提出了一个基于成对区域的单幅图像阴影去除方法,使用自定义的低级特征检测阴影,然后基于物理光照模型去除阴影,但处理高清图像效率低下;
上述方法均不能建立有效的背景模型,处理后的无阴影视频在时空域上不能保持一致连贯性。为此,本文提出一种视频阴影去除框架,能够高效处理静态相机或自由移动相机捕获到的视频。首先,使用连贯性的块匹配机制和局部光照传播最优化技术为每个阴影块恢复其光照信息;然后,进行全局优化和阴影边界处理。经过一系列实验,与已有的基于光照传播的视频阴影去除方法相比,所提方法在效率和效果上均有较大的提升。
1 视频阴影检测
给定输入视频V(x,y,t)。其中:x,y为像素点的坐标;t为时间分量。首先,基于尺度不变特征变换(Scale Invariant Feature Transform,SIFT)对视频进行均值漂移(Mean Shift)分割;然后,对分割后场景阴影图像的各个区域分别提取色彩及纹理信息;最后利用文献[1]中的支持向量机(Support Vector Machine,SVM)方法检测出视频中的阴影区域。 1.1 视频分割
在阴影检测环节,为了准确检测出阴影的位置,使用对光照、旋转、缩放鲁棒的SIFT特征向量对视频进行高维Mean Shift分割。
4 结语
传统方法处理自由移动相机捕获到的视频时,会出现时空不连贯现象,为此本文提出了一种区域配对引导的光照传播视频阴影去除方法。首先,基于SIFT特征向量对输入视频进行分割,进而使用支持向量机分类器对分割区域进行分类,从而得到输入视频的阴影检测结果;然后,将输入视频分割成重叠的二维图像块,为输入视频建立马尔可夫随机场,基于光流引导的块匹配机制完成阴影区域的标号问题;最后,使用光照传播技术,先完成图像块的局部光照传播操作,再进行全局优化,即可得到时空连贯的无阴影视频。实验结果表明,本文方法具有较高的阴影检测识别率,与文獻[14]和文献[17]的方法相比在阴影综合评价指标上有一定提升;复原后的图像误差较小,在像素的均方根误差上有所降低;且具有较高的运行效率。未来的工作将考虑结合多尺度的方法,进一步提高算法在面对纹理复杂的视频时的效果。
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