Shadow Optimization from Structured Deep Edge Detection

Abstract

We present a novel learning-based framework for shadow detection from a single image. The local structure of shadow boundaries as well as the global interactions of the shadow and non-shadow regions remain largely unexploited by previous learning-based approaches. In this paper, we propose an efficient structured labelling framework for shadow detection from a single image. A convolutional Neural Networks framework is designed to capture the local structure information of shadow edge and to learn the most relevant features. We further propose and formulate a global shadow optimization framework which can model the complex global interactions over the shadow and light regions. Using the shadow edges detected by our proposed method, the shadow map can be solved by efficient least-square optimization. Our proposed framework is efficient and achieves state-of-the-art results on the major shadow benchmark databases collected under a variety of conditions.