Conference paper
Automatic feature learning for robust shadow detection
2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.1939-1946
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2014 (Columbus, OH, 23/06/2014–28/06/2014)
2014
Abstract
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 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 7-layer network architecture of each ConvNet consists of alternating convolution and sub-sampling layers. The proposed framework learns features at the super-pixel level and along the object boundaries. In both cases, features are extracted using a context aware window centered at interest points. The predicted posteriors based on the learned features are fed to a conditional random field model to generate smooth shadow contours. Our proposed framework consistently performed better than the state-of-the-art on all major shadow databases collected under a variety of conditions.
Details
- Title
- Automatic feature learning for robust shadow detection
- Authors/Creators
- S.H. Khan (Author/Creator)M. Bennamoun (Author/Creator)F. Sohel (Author/Creator)R. Togneri (Author/Creator)
- Publication Details
- 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.1939-1946
- Conference
- IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2014 (Columbus, OH, 23/06/2014–28/06/2014)
- Identifiers
- 991005540449007891
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Conference paper
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