Automatic Shadow Detection and Removal from a Single Image

作者：Khan, Salman H.^*; Bennamoun, Mohammed^*; Sohel, Ferdous^*; Togneri, Roberto^*

来源：IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(3): 431-446.

DOI：10.1109/TPAMI.2015.2462355

• Feature learning
• Bayesian shadow removal
• conditional random field
• convnets
• shadow detection
• shadow matting

We present a framework to automatically detect and remove shadows in real world scenes from a single image. Previous works on shadow detection put a lot of effort in designing shadow variant and invariant hand-crafted features. In contrast, our framework automatically learns the most relevant features in a supervised manner using multiple convolutional deep neural networks (ConvNets). The features are learned at the super-pixel level and along the dominant boundaries in the image. The predicted posteriors based on the learned features are fed to a conditional random field model to generate smooth shadow masks. Using the detected shadow masks, we propose a Bayesian formulation to accurately extract shadow matte and subsequently remove shadows. The Bayesian formulation is based on a novel model which accurately models the shadow generation process in the umbra and penumbra regions. The model parameters are efficiently estimated using an iterative optimization procedure. Our proposed framework consistently performed better than the state-of-the-art on all major shadow databases collected under a variety of conditions.

• 出版日期2016-3