Shadow Detection from Real Images and Removal Using Image Processing

Abstract

Automatic shadow detection and removal have been used in many image processing systems such as video surveillance, scene interpretations, and object recognition. Ignoring the presence of shadows in images can cause serious problems such as object merging, object loss, misinterpretation, and alteration makeup in visual processing applications such as segment, group analysis, and follow-up. Many algorithms had it proposed to books, related to the acquisition and removal of images and videos. Comparative testing and capacity building of existing methods in the video has already been reported, but we do not have the same in case the images are still standing. This paper provides the complete existing dignity detection survey and removal technique reported in the current situation image. The test metrics involved in strategies for finding and removing strategies are also discussed with the inefficiencies of common metrics such as the accuracy of the pixel, precision, recall, and F-score in the acquisition phase which is also checked. Plenty and quantity of the selected methods are also tested. Ku to our knowledge all of this is a special first article that discusses ways to detect and remove shadows from real photos.