红外技术, 2017, 39 (8): 728, 网络出版: 2017-10-30   

基于深度卷积神经网络的红外场景理解算法

Infrared Scene Understanding Algorithm Based on Deep Convolutional Neural Network
王晨 1,2,3汤心溢 1,3高思莉 1,3
作者单位
1 中国科学院上海技术物理研究所,上海 200083
2 中国科学院大学,北京 100049
3 中国科学院红外探测与成像技术重点实验室,上海 200083
引用该论文

王晨, 汤心溢, 高思莉. 基于深度卷积神经网络的红外场景理解算法[J]. 红外技术, 2017, 39(8): 728.

WANG Chen, TANG Xinyi, GAO Sili. Infrared Scene Understanding Algorithm Based on Deep Convolutional Neural Network[J]. Infrared Technology, 2017, 39(8): 728.

参考文献

[1] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2015: 1337-1342.

[2] Zheng S, Jayasumana S, Romeraparedes B, et al. Conditional random fields as recurrent neural networks[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2015:1529-1537.

[3] Chen L C, Papandreou G, Kokkinos I, et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs[J]. Computer Science, 2014(4):357-361.

[4] Noh H, Hong S, Han B. Learning deconvolution network for semantic segmentation[C]//Proceedings of the IEEE International Conference on Computer Vision, 2015: 1520-1528.

[5] Pizer S M, Amburn E P, Austin J D, et al. Adaptive Histogram equalization and its variations[J]. Computer Vision, Graphics, and Image Processing, 1987, 39(3): 355-368.

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[8] Cordts M, Omran M, Ramos S, et al. The cityscapes dataset for semantic urban scene understanding[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 3213-3223.

王晨, 汤心溢, 高思莉. 基于深度卷积神经网络的红外场景理解算法[J]. 红外技术, 2017, 39(8): 728. WANG Chen, TANG Xinyi, GAO Sili. Infrared Scene Understanding Algorithm Based on Deep Convolutional Neural Network[J]. Infrared Technology, 2017, 39(8): 728.

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