In case of complex textures,existing static shadow detection and removal algorithms are prone to false detection of the pixels.To solve this problem,a static shadow detection and removal algorithm based on support vec...In case of complex textures,existing static shadow detection and removal algorithms are prone to false detection of the pixels.To solve this problem,a static shadow detection and removal algorithm based on support vector machine(SVM)and region sub-block matching is proposed.Firstly,the original image is segmented into several superpixels,and these superpixels are clustered using mean-shift clustering algorithm in the superpixel sets.Secondly,these features such as color,texture,brightness,intensity and similarity of each area are extracted.These features are used as input of SVM to obtain shadow binary images through training in non-operational state.Thirdly,soft matting is used to smooth the boundary of shadow binary graph.Finally,after finding the best matching sub-block for shadow sub-block in the illumination region based on regional covariance feature and spatial distance,the shadow weighted average factor is introduced to partially correct the sub-block,and the light recovery operator is used to partially light the sub-block.The experimental results show the number of false detection of the pixels is reduced.In addition,it can remove shadows effectively for the image with rich textures and uneven shadows and make a natural transition at the boundary between shadow and light.展开更多
文摘针对运动车辆阴影带来车辆合并及形状失真的问题,提出一种基于Fast RCNN模型的车辆阴影检测去除算法。采用Selective search法对视频车辆图像提取多个车辆候选目标矩形区域,采用Hessenberg分解法将运动车辆和其阴影区域分开;利用深度网络提取阴影特征,用PCA分析法检测阴影,训练优化该网络,识别移动阴影中包含的车辆区域,实现快速去除阴影的效果。实验结果表明,该方法有效解决传统算法多车辆阴影检测去除效率低下问题,平均检测精度mAP(mean average precision)提高2.78%,为智能交通系统提供良好技术基础。
基金University and College Scientific Research Fund of Gansu Province(No.2017A-026)Foundation of A hundred Youth Talents Training Program of Lanzhou Jiaotong University。
文摘In case of complex textures,existing static shadow detection and removal algorithms are prone to false detection of the pixels.To solve this problem,a static shadow detection and removal algorithm based on support vector machine(SVM)and region sub-block matching is proposed.Firstly,the original image is segmented into several superpixels,and these superpixels are clustered using mean-shift clustering algorithm in the superpixel sets.Secondly,these features such as color,texture,brightness,intensity and similarity of each area are extracted.These features are used as input of SVM to obtain shadow binary images through training in non-operational state.Thirdly,soft matting is used to smooth the boundary of shadow binary graph.Finally,after finding the best matching sub-block for shadow sub-block in the illumination region based on regional covariance feature and spatial distance,the shadow weighted average factor is introduced to partially correct the sub-block,and the light recovery operator is used to partially light the sub-block.The experimental results show the number of false detection of the pixels is reduced.In addition,it can remove shadows effectively for the image with rich textures and uneven shadows and make a natural transition at the boundary between shadow and light.
