Automatic detection and removal of static shadows

作     者：HOU Tao WU Hai-ping 侯涛;伍海萍

作者机构：School of Automation and Electrical EngineeringLanzhou Jiaotong UniversityLanzhou 730070China 

出 版 物：《Journal of Measurement Science and Instrumentation》 (测试科学与仪器（英文版）)

年 卷 期：2020年第11卷第4期

页      面：343-350页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

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 

主　　题：shadow detection shadow removal feature extraction support vector machine(SVM) block matching light recovery operator 

摘      要：In case of complex textures,existing static shadow detection and removal algorithms are prone to false detection of the *** solve this problem,a static shadow detection and removal algorithm based on support vector machine(SVM)and region sub-block matching is ***,the original image is segmented into several superpixels,and these superpixels are clustered using mean-shift clustering algorithm in the superpixel ***,these features such as color,texture,brightness,intensity and similarity of each area are *** features are used as input of SVM to obtain shadow binary images through training in non-operational ***,soft matting is used to smooth the boundary of shadow binary ***,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 *** experimental results show the number of false detection of the pixels is *** 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.