Open Access
| Issue |
MATEC Web Conf.
Volume 277, 2019
2018 International Joint Conference on Metallurgical and Materials Engineering (JCMME 2018)
|
|
|---|---|---|
| Article Number | 02036 | |
| Number of page(s) | 7 | |
| Section | Data and Signal Processing | |
| DOI | https://doi.org/10.1051/matecconf/201927702036 | |
| Published online | 02 April 2019 | |
- Wang W H, Liu Y S. Image anomalies analysis and detection algorithm for surveillance video[J]. Journal of Central China Normal University (Natural Sciences), 2015, 49(05): 692-695. [Google Scholar]
- Cheng C S. Design and implementation of the real-time detection system for surveillance video quality[D]. Zhejiang University of Technology, 2014. [Google Scholar]
- Xia Y J, Sun H. Anomaly detection and quality diagnosis of surveillance video[J]. Computer Applications and Software, 2016, 33(06): 163-167+211. [Google Scholar]
- Wu M Y, Chen L, Tian J. Video image distortion detection and classification based on CNN[J]. Application Research of Computers, 2016, 33(09): 2827-2830. [Google Scholar]
- Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556, 2014. [Google Scholar]
- Szegedy C, Liu W, Jia Y, & RABINOVICH, A. et al. Going deeper with convolutions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition, 2015:1-9. [Google Scholar]
- He K, Zhang X, Ren S., et al Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition, 2016: 770-778. [Google Scholar]
- Shin H C, Roth H R, Gao M., et al Deep convolutional neural networks for computeraided detection: CNN architectures, dataset characteristics and transfer learning[J]. IEEE transactions on medical imaging, 2016, 35(5): 1285-1298. [Google Scholar]
- Donahue J, Jia Y, Vinyals O, et al. Decaf: A deep convolutional activation feature for generic visual recognition[C]//International conference on machine learning. 2014:647-655. [Google Scholar]
- Tajbakhsh N, Shin J Y, GURUDU S R., et al Convolutional neural networks for medical image analysis: Full training or fine tuning?[J]. IEEE transactions on medical imaging, 2016, 35(5): 1299-1312. [Google Scholar]
- Gao F, Wang Y, Li P., et al DeepSim: Deep similarity for image quality assessment[J]. Neurocomputing, 2017, 257: 104-114. [CrossRef] [Google Scholar]
- Vapnik V. Statistical learning theory[M]. New York: Wiely, 1998. [Google Scholar]
- Hsu C W, Lin C J. A comparison of methods for multiclass support vector machines[J]. IEEE Transactions on Neural Networks, 2002, 13(4):1026. [CrossRef] [Google Scholar]
- Shahriari B, SWERSKY K, Wang Z., et al Taking the human out of the loop: A review of bayesian optimization[J]. Proceedings of the IEEE, 2016, 104(1): 148-175. [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.