双层SVM的阴影检测与基于窗口融合的阴影恢复

阴影的存在对于机器视觉的分析如区域的边缘提取，目标识别以及图像匹配等往往具有一定的干扰，而现如今提出的算法往往并不能很好地处理阴影区域的纹理和边缘。主要针对静态阴影图像提出了一种成对区域阴影检测和恢复的方法。阴影检测：通过提取阴影和非阴影区域之间边缘两侧颜色、纹理、方向和距离特征采用双层SVM训练器对阴影区域进行检测，不仅能检测出阴影区域，同时能够检测出与此阴影区域在同一表面的非阴影区域。阴影恢复：采用提出的自适应步长的窗口匹配方法获得阴影区域的采样窗口的最佳匹配窗口，采用窗口融合的方式初步恢复阴影区域，然后使用提出的迭代的方式对图像纹理进行增强，最后用快速修复方法（FMM）去除阴影与非阴影区域的微弱边缘。Opencv仿真证明，这样不仅可以提高阴影区域检测准确率，而且能够很好地保存纹理信息。

Shadow detection using double-layer SVM and shadow recovery based on windows merge

The shadow usually have some interference on the analysis of machine vision, the algorithms of edge extraction, object recognition and image matching, while many proposed algorithms often can’t process the texture and edge of the shadow areas well.This paper presents an approach to shadow detection and removal for the texture-consistent shadow.Shadow detection：Double-layer SVM trainer is used in this paper to detecte shadow regions by sampling colors, texture, directions and distances features of the shadow regions and lit regions between shadow edges, which can detect both shadow and lit regions corresponding to the shadow region.Shadow recovery：Adaptive steps of windows matched algorithm is used to get the optional windows and recover shadows preliminary through windows merging, then enhance texture iteratively.Finally, the Fast Marching Method（FMM） is adopted to remove slight shadow edges.The result of Opencv experiment shows that this algorithm can not only improve the accuracy of the shadow detection but also have a better removal effect on texture and edge of shadow areas.