IEEE Trans Pattern Anal Mach Intell. 2016 Mar;38(3):431-46. doi: 10.1109/TPAMI.2015.2462355.
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.
我们提出了一个从单张图像中自动检测和去除真实场景中阴影的框架。以前的阴影检测工作在设计阴影变体和不变的手工制作特征方面投入了大量精力。相比之下，我们的框架使用多个卷积深度神经网络（ConvNets）以监督的方式自动学习最相关的特征。特征是在超像素级别和图像的主导边界上学习的。基于学习到的特征预测的后验概率被输入到条件随机场模型中，以生成平滑的阴影掩模。利用检测到的阴影掩模，我们提出了一种贝叶斯公式，以准确提取阴影遮罩并随后去除阴影。该贝叶斯公式基于一个新颖的模型，该模型可以准确地模拟本影和半影区域的阴影生成过程。模型参数使用迭代优化过程进行有效估计。我们提出的框架在各种条件下收集的所有主要阴影数据库上的表现均优于最先进的方法。
