The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Interactive deep learning method for segmenting moving objects
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Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,395
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
(2016) · Cited by 11,679
Adaptive background mixture models for real-time tracking
Stauffer, C., & Grimson, W. E. L. (2003) · Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) · doi:10.1109/cvpr.1999.784637 · Cited by 6,923
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061
GrabCut: Interactive foreground extraction using iterated graph cuts
Rother, C., Kolmogorov, V., & Blake, A. (2004) · ACM Transactions on Graphics · doi:10.1145/1015706.1015720 · Cited by 5,703
Cited by
Online Adaptation of Convolutional Neural Networks for Video Object Segmentation
Voigtlaender, P., & Leibe, B. (2017) · Procedings of the British Machine Vision Conference 2017 · doi:10.5244/c.31.116 · Cited by 443
Bouwmans, T., Javed, S., Sultana, M., & Jung, S. K. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.04.024 · Cited by 396
Background subtraction in real applications: Challenges, current models and future directions
Garcia-Garcia, B., Bouwmans, T., & Rosales Silva, A. J. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2019.100204 · Cited by 337
Foreground segmentation using convolutional neural networks for multiscale feature encoding
Lim, L. A., & Yalim Keles, H. (2018) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.08.002 · Cited by 305
Kwon, B. C., Choi, M.-J., Kim, J. T., Choi, E., Kim, Y. B., Kwon, S., Sun, J., & Choo, J. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2865027 · Cited by 294
Metadata
- Authors
- Wang, Y., Luo, Z., & Jodoin, P.-M.
- Venue
- Pattern Recognition Letters
- Publisher
- Elsevier BV
- Published
- 2017-09
- Volume
- 96
- Pages
- 66-75
- ISSN
- 0167-8655 (ISSN-L)
- OpenAlex
- W2525668722
- Cited by
- 334
- References
- 33
- FWCI
- 24.64
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Interactive deep learning method for segmenting moving objects. https://app.cochat.ai/papers/10.1016/j.patrec.2016.09.014
- The paper
- Wang, Y., Luo, Z., & Jodoin, P.-M. (2017). Interactive deep learning method for segmenting moving objects. Pattern Recognition Letters. https://doi.org/10.1016/j.patrec.2016.09.014
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
