The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Automatic Shadow Detection and Removal from a Single Image
Open accessAbstract
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.
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Cites
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680
SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Süsstrunk, S. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.120 · Cited by 9,201
Efficient Graph-Based Image Segmentation
Felzenszwalb, P. F., & Huttenlocher, D. P. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000022288.19776.77 · Cited by 6,176
Contour Detection and Hierarchical Image Segmentation
Arbeláez, P., Maire, M., Fowlkes, C., & Malik, J. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.161 · Cited by 5,454
Multi-column deep neural networks for image classification
Ciresan, D., Meier, U., & Schmidhuber, J. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248110 · Cited by 3,720
Region Filling and Object Removal by Exemplar-Based Image Inpainting
Criminisi, A., Perez, P., & Toyama, K. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2004.833105 · Cited by 3,258 · cited 1×
“[47].”
Reinhard, E., Adhikhmin, M., Gooch, B., & Shirley, P. (2001) · IEEE Computer Graphics and Applications · doi:10.1109/38.946629 · Cited by 2,937
Pérez, P., Gangnet, M., & Blake, A. (2003) · ACM SIGGRAPH 2003 Papers · doi:10.1145/1201775.882269 · Cited by 2,020
Graph Cuts and Efficient N-D Image Segmentation
Boykov, Y., & Funka-Lea, G. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-006-7934-5 · Cited by 1,910
Metadata
- Authors
- Khan, S. H., Bennamoun, M., Sohel, F., & Togneri, R.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2016-03-01
- Volume
- 38
- Issue
- 3
- Pages
- 431-446
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2252574053
- Cited by
- 283
- References
- 38
- FWCI
- 20.80
- Type
- journal article
- Language
- en
- Open access
- green
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