Journal article
Automatic shadow detection and removal from a single image
IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.38(3), pp.431-446
2015
Abstract
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.
Details
- Title
- Automatic shadow detection and removal from a single image
- Authors/Creators
- S. Khan (Author/Creator) - The University of Western AustraliaM. Bennamoun (Author/Creator) - The University of Western AustraliaF. Sohel (Author/Creator) - Murdoch UniversityR. Togneri (Author/Creator) - The University of Western Australia
- Publication Details
- IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.38(3), pp.431-446
- Publisher
- IEEE
- Identifiers
- 991005543094107891
- Copyright
- © 2015 IEEE
- Murdoch Affiliation
- School of Engineering and Information Technology
- Language
- English
- Resource Type
- Journal article
Metrics
75 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.17 Computer Vision & Graphics
- 4.17.861 Color Imaging
- Web Of Science research areas
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic
- ESI research areas
- Engineering