| Citation: |
HE Yide, ZHU Bin, WANG Xun, CHEN Hao, JIA Jing. Simulation model fidelity evaluation method based on key features[J]. LASER TECHNOLOGY, 2020, 44(4): 515-519. DOI: 10.7510/jgjs.issn.1001-3806.2020.04.020
|
| [1] |
QU Y, TIAN X F, MA L H, et al. Application of VV & A in infrared target simulator and its fidelity evaluation[J]. Infrared, 2011, 32(7):28-33(in Chinese). http://en.cnki.com.cn/Article_en/CJFDTOTAL-HWAI201107006.htm
|
| [2] |
ZHOU Y F, ZHAO J, WANG D Y, et al. Detection and evaluation of fidelity and reliability of infrared target ship[J]. Infrared, 2013, 34(7):39-44(in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hongw201307009
|
| [3] |
LI H, WANG M, GUO Zh H, et al. Fidelity research on IR capturing and tracking device based on digital image injection simulation test[J]. Modern Defense Technology, 2018, 46(2): 193-201(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/xdfyjs201802030
|
| [4] |
MA T, GENG M. Simulation of infrared target detection system[J]. Electro-Optic Technology Application, 2017, 32(2):62-67(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/hwyjggc201804020
|
| [5] |
GONG Y, WANG Q Q, SHAN B, et al. Quantized fidelity evaluation scheme of laser target simulator[J].Infrared and Laser Engineering, 2016, 45(12):124-129(in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hwyjggc201612021
|
| [6] |
TIAN X F, MA L H, ZHAO Sh H, et al. Fidelity evaluation scheme for IR target simulation[J].Semiconductor, 2012, 33(1):135-140(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/bdtgd201201033
|
| [7] |
LOWE D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2):91-110. http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ025429678/
|
| [8] |
BAY H, TUYTELAARS T, van GOOL L. Surf: Speeded up robust features[C]//The 9th European Conference on Computer Vision. Graz, Austria: Springer, 2006: 404-417. 10.1007/11744023_32
|
| [9] |
BOLME D S, BEVERIDGE J R, DRAPER B A, et al. Visual object tracking using adaptive correlation filters[C]//Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2010: 2544-2550. 10.1109/CVPR.2010.5539960
|
| [10] |
KRIZHEVSKY A, SUTSKEVER I, HINTON G. Image net classification with deep convolutional neural networks[C]// Twenty-fifth Conference on Neural Information Processing Systems. New York, USA: IEEE, 2012: 1097-1105. 10.1145/3065386
|
| [11] |
NEYSHABUR B, BHOJANAPALLI S, MCALLESTER D, et al. Exploring generalization in deep learning[C]//Thirty-first Conference on Neural Information Processing Systems. New York, USA: IEEE, 2017: 5949-5958. DOI: 10.5555/3295222.3295344
|
| [12] |
SONODA S, MURATA N. Neural network with unbounded activation functions is universal approximator[J]. Applied and Computational Harmonic Analysis, 2015, 43(2):233-268. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=b0b2ed9ce0b134e51752190574bd8c70
|
| [13] |
MA S, BELKIN M. Diving into the shallows: a computational perspective on large-scale shallow learning[C]//Thirty-first Conference on Neural Information Processing Systems. New York, USA: IEEE, 2017: 3781-3790. https://arxiv.org/abs/1703.10622
|
| [14] |
HENRIQUES J F, CASEIRO R, MARTINS P, et al. High-speed tracking with kernelized correlation filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3):583-596. DOI: 10.1109/TPAMI.2014.2345390
|
| [15] |
REDMON J, FARHADI A. YOLO9000: Better, faster, stronger[C]//Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2017: 6517-6525. 10.1109/CVPR.2017.690
|
| [16] |
DONAHUE J, JIA Y, VINYALS O, et al. DeCAF: A deep convolutional activation feature for generic visual recognition[C]//Proceedings of the 31st International Conference on Machine Learning. New York, USA: IEEE, 2014: 647-655. 10.1097/00003643-201406001-00333
|
| [17] |
ZEILER M D, FERGUS R. Visualizing and understanding convolutional networks[C]//The 13th European Conference on Computer Vision. Zurich, Switzerland: Springer, 2014: 818-833. https://arxiv.org/abs/1311.2901
|
| [18] |
LU Z, PU H, WANG F, et al. The expressive power of neural network: A view from the width[C]//Thirty-first Conference on Neural Information Processing Systems. New York, USA: IEEE, 2017: 6231-6239. DOI: 10.5555/3295222.3295371
|
| [19] |
YOSINSKI J, CLUNE J, BENGIO Y, et al. How transferable are features in deep neural networks?[C]//Twenty-eighth Conference on Neural Information Processing Systems. New York, USA: IEEE, 2014: 3320-3328. DOI: 10.5555/2969033.2969197
|
| [20] |
HU H, ZHOU G T, DENG Z, et al. Learning structured inference neural networks with label relations[C]//Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2016: 2960-2968. 10.1109/CVPR.2016.323
|
| [21] |
DENG Z, VAHDAT A, HU H, et al. Structure inference machine: Recurrent neural networks for analyzing relations in group activity recognition[C]//Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2016: 4772-4781. 10.1109/CVPR.2016.516
|
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