Shadow Segmentation and Classification in a Constrained Environment

Abstract

A shadow identification and classification method for real images is developed in this paper. The method is based on the extensive analysis of shadow intensity and shadow geometry in an environment with simple objects and a single area light source. The procedure for identifying shadows is divided into three processes: low level, middle level, and high level. The low level process extracts dark regions from images. Dark regions contain both shadows and surfaces with low reflectance. The middle level process performs feature analysis on dark regions, including detecting vertices on the outlines of dark regions, identifying penumbrae in dark regions. classifying the subregions in dark regions as self-shadows or cast shadows, and finding object regions adjacent to dark regions. The high level process integrates the infonnation derived from the previous processes and confirms shadows among the dark regions.