1 |
X. Wang, T. X. Han, and S. Yan, "An HOG-LBP human detector with partial occlusion handling," in 2009 IEEE 12th International Conference on Computer Vision, pp. 32-39, 2009. https://doi.org/10.1109/iccv.2009.5459207
|
2 |
T. Watanabe, S. Ito, and K. Yokoi, "Co-occurrence Histograms of Oriented Gradients for Pedestrian Detection," Springer, Berlin, Heidelberg, pp. 37-47, 2009. https://doi.org/10.1007/978-3-540-92957-4_4
|
3 |
C. Zhan, X. Duan, S. Xu, Z. Song, and M. Luo, "An Improved Moving Object Detection Algorithm Based on Frame Difference and Edge Detection," in Fourth International Conference on Image and Graphics (ICIG 2007), pp. 519-523, 2007. https://doi.org/10.1109/icig.2007.153
|
4 |
U. Jain, K. Choudhary, S. Gupta, and M. J. Pemeena Privadarsini, "Analysis of Face Detection and Recognition Algorithms Using Viola Jones Algorithm with PCA and LDA," in 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), pp. 945-950, 2018. https://doi.org/10.1109/icoei.2018.8553811
|
5 |
M. Grega, S. Lach, and R. Sieradzki, "Automated recognition of firearms in surveillance video," in 2013 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), pp. 45-50, 2013. https://doi.org/10.1109/cogsima.2013.6523822
|
6 |
L. Zhang, L. Lin, X. Liang, and K. He, "Is Faster R-CNN Doing Well for Pedestrian Detection?," Springer, Cham, pp. 443-457, 2016. https://doi.org/10.1007/978-3-319-46475-6_28
|
7 |
D. G. Lowe, "Object recognition from local scale-invariant features," in Proceedings of the Seventh IEEE International Conference on Computer Vision, vol.2, pp. 1150-1157, 1999. https://doi.org/10.1109/iccv.1999.790410
|
8 |
Y. LeCun et al., "Backpropagation Applied to Handwritten Zip Code Recognition," Neural Comput., vol. 1, no. 4, pp. 541-551, 1989. https://doi.org/10.1162/neco.1989.1.4.541
DOI
|
9 |
D. Zhang, D. Ding, J. Li, and Q. Liu, "PCA Based Extracting Feature Using Fast Fourier Transform for Facial Expression Recognition," in Transactions on Engineering Technologies, Dordrecht: Springer Netherlands, pp. 413-424, 2015. https://doi.org/10.1007/978-94-017-9588-3_31
|
10 |
H. Zou and Z. Jin, "Comparative Study of Big Data Classification Algorithm Based on SVM," in 2018 Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSQRWC), pp. 1-3, 2018. https://doi.org/10.1109/csqrwc.2018.8455423
|
11 |
J. Deng, W. Dong, R. Socher, L.-J. Li, Kai Li, and Li Fei-Fei, "ImageNet: A large-scale hierarchical image database," in 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248-255, 2009. https://doi.org/10.1109/cvpr.2009.5206848
|
12 |
A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," Communications of the ACM, vol. 60, no. 6, pp. 84-90, May 2017. https://doi.org/10.1145/3065386
DOI
|
13 |
K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," 2014. https://arxiv.org/abs/1409.1556
|
14 |
G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, "Densely Connected Convolutional Networks," in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700-4708, 2017. https://doi.org/10.1109/cvpr.2017.243
|
15 |
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the Inception Architecture for Computer Vision," in Proceedings of the IEEE conference on computer vision and pattern recognition, pp.2818-2826,2016. https://doi.org/10.1109/cvpr.2016.308
|
16 |
K. Weiss, T. M. Khoshgoftaar, and D. Wang, "A survey of transfer learning," J. Big Data, vol. 3, no. 1, p. 9, Dec. 2016. https://doi.org/10.1186/s40537-016-0043-6
DOI
|
17 |
N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection," in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), vol. 1, pp. 886-893, 2005. https://doi.org/10.1109/cvpr.2005.177
|
18 |
F. Chollet, "Xception: Deep Learning with Depthwise Separable Convolutions," in Proceedings of the IEEE conference on computer vision and pattern recognition., pp. 1251-1258., 2017. https://doi.org/10.1109/cvpr.2017.195
|
19 |
T.-J. Yang, Y.-H. Chen, and V. Sze, "Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5687-5695, 2017. https://doi.org/10.1109/cvpr.2017.643
|
20 |
A. G. Howard et al., "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications," 2017. https://arxiv.org/abs/1704.04861
|
21 |
S. J. Pan and Q. Yang, "A Survey on Transfer Learning," in IEEE Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345-1359, Oct. 2010. https://doi.org/10.1109/tkde.2009.191
DOI
|
22 |
C. Szegedy et al., "Going Deeper with Convolutions,"in Proceedings of the IEEE conference on computer vision and pattern recognition., pp.. 1-9, 2015. https://doi.org/10.1109/cvpr.2015.7298594
|
23 |
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi, "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning," in Thirty-First AAAI Conference on Artificial Intelligence, 2017. https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/viewPaper/14806
|
24 |
J. Donahue et al.,"Decaf: A deep convolutional activation feature for generic visual recognition," in Proceedings of the 31st International Conference on Machine Learning, pp.647-655, 2014. https://dl.acm.org/citation.cfm?id=3044879
|
25 |
F. Chollet and others, "Keras: The Python Deep Learning library," Astrophys. Source Code Libr. Rec. ascl1806.022, 2018. https://ui.adsabs.harvard.edu/abs/2018ascl.soft06022C/abstract
|
26 |
M. Talo et al., "Application of deep transfer learning for automated brain abnormality classification using MR images," Cognitive Systems Research, vol. 54, pp. 176-188, 2019. https://doi.org/10.1016/j.cogsys.2018.12.007
DOI
|
27 |
S. Khan, N. Islam, Z. Jan, I. U. Din, J. J. C. Rodrigues, "A novel deep learning based framework for the detection and classification of breast cancer using transfer learning," Pattern Recognition Letters, vol. 125, pp. 1-6, 2019. https://doi.org/10.1016/j.patrec.2019.03.022
DOI
|
28 |
M. Abadi et al., "TensorFlow: A System for Large-Scale Machine Learning," in 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI}16), pp. 265-283, 2016. https://www.usenix.org/conference/osdi16/technical-sessions/presentation/abadi
|