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 …

In recent years, the analysis of natural image has made great progress while the image of
the intrinsic component analysis can solve many computer vision problems, such as the …

In this paper, we present a novel shadow detection framework by investigating the mutual
complementary mechanisms contained in this specific task. Our method is based on a key …

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 …

The objective of this work is to detect shadows in images. We pose this as the problem of
labeling image regions, where each region corresponds to a group of superpixels. To …

Shadow detection and shadow removal are fundamental and challenging tasks, requiring
an understanding of the global image semantics. This paper presents a novel deep neural …

We propose a novel deep learning method for shadow removal. Inspired by physical models
of shadow formation, we use a linear illumination transformation to model the shadow effects …

Shadow removal is a challenging task as it requires the detection/annotation of shadows as
well as semantic understanding of the scene. In this paper, we propose an automatic and …

We present a practical framework to automatically detect shadows in real world scenes from
a single photograph. Previous works on shadow detection put a lot of effort in designing …

Shadow removal is an essential task in computer vision and computer graphics. Recent
shadow removal approaches all train convolutional neural networks (CNN) on real paired …