42 research outputs found
Direction-aware Spatial Context Features for Shadow Detection
Shadow detection is a fundamental and challenging task, since it requires an
understanding of global image semantics and there are various backgrounds
around shadows. This paper presents a novel network for shadow detection by
analyzing image context in a direction-aware manner. To achieve this, we first
formulate the direction-aware attention mechanism in a spatial recurrent neural
network (RNN) by introducing attention weights when aggregating spatial context
features in the RNN. By learning these weights through training, we can recover
direction-aware spatial context (DSC) for detecting shadows. This design is
developed into the DSC module and embedded in a CNN to learn DSC features at
different levels. Moreover, a weighted cross entropy loss is designed to make
the training more effective. We employ two common shadow detection benchmark
datasets and perform various experiments to evaluate our network. Experimental
results show that our network outperforms state-of-the-art methods and achieves
97% accuracy and 38% reduction on balance error rate.Comment: Accepted for oral presentation in CVPR 2018. The journal version of
this paper is arXiv:1805.0463
Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network
In recent years, various shadow detection methods from a single image have
been proposed and used in vision systems; however, most of them are not
appropriate for the robotic applications due to the expensive time complexity.
This paper introduces a fast shadow detection method using a deep learning
framework, with a time cost that is appropriate for robotic applications. In
our solution, we first obtain a shadow prior map with the help of multi-class
support vector machine using statistical features. Then, we use a semantic-
aware patch-level Convolutional Neural Network that efficiently trains on
shadow examples by combining the original image and the shadow prior map.
Experiments on benchmark datasets demonstrate the proposed method significantly
decreases the time complexity of shadow detection, by one or two orders of
magnitude compared with state-of-the-art methods, without losing accuracy.Comment: 6 pages, 5 figures, Submitted to IROS 201