When the microLED is in the forward working direction, it is difficult to precisely adjust its voltage to obtain different brightness. Moreover, when the microLED/OLED is turned on, they will be in a closed state for a long time, causing the image display brightness to be deteriorated by the human eye. In order to solve these problems, this paper proposes a dual-frame decentralized fusion scanning strategy to achieve different brightness by adjusting the microLED/OLED on-time. Firstly, the method de-weights the data bits and inserts their on-times into the closed time. Then the data bit weights are double-frame fused after decentralization. Finally, the scanning order of the data bits is redefined. According to the proposed scanning strategy, we designed a scanning controller to drive digital on-silicon microdisplay. The results show that the dual-frame decentralized fusion scan proposed in this paper can accurately adjust the luminance of microLED/OLED and improve the brightness of the image observed by human eyes. Compared with other scanning strategies, the scanning strategy improves the scanning efficiency to 93.75%, the field frequency is increased to 2040 Hz, the scanning clock frequency is 102.36 MHz, and the scanning data bandwidth is reduced. The feasibility of the scan controller is proved by testing at last.
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Opto-Electronic Engineering
ISSN: 1003-501X
CN: 51-1346/O4
Monthly, included in CA, Scopus, CSCD
CN: 51-1346/O4
Monthly, included in CA, Scopus, CSCD
Soft multilabel learning and deep feature fusion for unsupervised person re-identification
Author Affiliations
Correspondence: zbh_wj2004@imust.edu.cn
First published at:Dec 22, 2020
Abstract
References
[1] Xiong F, Xiao Y, Cao Z G, et al. Good practices on building effective CNN baseline model for person re-identification[J]. Proceedings of SPIE, 2019, 11069: 110690I.
[2] Wang S Q, Xu X, Liu L, et al. Multi-level feature fusion model-based real-time person re-identification for forensics[J]. Journal of Real-Time Image Processing, 2020, 17(1): 73–81.
[3] Bak S, Carr P, Lalonde J F. Domain adaptation through synthesis for unsupervised person re-identification[J]. ECCV, 2018: 189–205.
[4] Ye M, Li J W, Ma A J, et al. Dynamic graph co-matching for unsupervised video-based person re-identification[J]. IEEE Transactions on Image Processing, 2019, 28(6): 2976–2990.
[5] Yu H X, Wu A C, Zheng W S. Cross-view asymmetric metric learning for unsupervised person re-identification[C]//Proceedings of 2017 IEEE International Conference on Computer Vision, Venice, 2017: 994–1002.
[6] Fan H H, Zheng L, Yan C G, et al. Unsupervised person re-identification: clustering and fine-tuning[J]. ACM Transactions on Multimedia Computing, Communications, and Applications, 2018, 14(4): 83.
[7] Wang J Y, Zhu X T, Gong S G, et al. Transferable joint attribute-identity deep learning for unsupervised person re-identification[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, 2018: 2275–2284.
[8] Wei L G, Zhang S l, Gao W, et al. Person transfer GAN to bridge domain gap for person re-identification[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, 2018: 79–88.
[9] Deng W J, Zheng L, Ye Q X, et al. Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, 2018: 994–1003.
[10] Zhong Z, Zheng L, Li S Z, et al. Generalizing a person retrieval model hetero-and homogeneously[C]//Proceedings of the European Conference on Computer Vision, Glasgow, 2018: 172–188.
[11] Yu H X, Zheng W S, Wu A C, et al. Unsupervised person re-identification by soft multilabel learning[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, 2019: 2148–2157.
[12] He R, Wu X, Sun Z N, et al. Wasserstein CNN: learning invariant features for NIR-VIS face recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 41(7): 1761–1773.
[13] Wang F, Xiang X, Cheng J, et al. NormFace: L2 hypersphere embedding for face verification[C]//Proceedings of the 25th ACM International Conference on Multimedia, California, Mountain View, 2017: 1041–1049.
[14] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, 2018: 7132–7141.
[15] Wang C, Zhang Q, Huang C, et al. Mancs: a multi-task attentional network with curriculum sampling for person re-identification[C]//Proceedings of the 15th European Conference on Computer Vision, Munich, 2018: 365–381.
[16] Fan H, Zheng L, Yan C, et al. Unsupervised Person Re-identification by Deep Learning Tracklet Association[J]. Acm Transactions on Multimedia Computing Communications & Applications, 2018, 14(4): 1–18.
[17] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 2016: 770–778.
[18] Wang Y, Wang L Q, You Y R, et al. Resource aware person re-identification across multiple resolutions[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, 2018: 8042–8051.
[19] Hu Y, Wen G H, Luo M N, et al. Competitive inner-imaging squeeze and excitation for residual network[Z]. arXiv: 1807.08920[cs: CV], 2018.
[20] Zheng L, Shen L Y, Tian L, et al. Scalable person re-identification: a benchmark[C]//Proceedings of 2015 IEEE International Conference on Computer Vision, Santiago, 2015: 1116–1124.
[21] Zheng Z D, Zheng L, Yang Y. Unlabeled samples generated by GAN improve the person re-identification baseline in vitro[C]//Proceedings of 2017 IEEE International Conference on Computer Vision, Venice, 2017: 3754–3762.
[22] Lin S, Li H L, Li C T, et al. Multi-task mid-level feature alignment network for unsupervised cross-dataset person re-identification[Z]. arXiv: 1807.01440[cs: CV], 2018.
[23] Yu H X, Wu A C, Zheng W S. Unsupervised person re-identification by deep asymmetric metric embedding[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 42(4): 956–973.
[24] Li Y J, Yang F E, Liu Y C, et al. Adaptation and re-identification network: an unsupervised deep transfer learning approach to person re-identification[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, 2018: 172–178.
[25] Lin Y T, Dong X Y, Zheng L, et al. A bottom-up clustering approach to unsupervised person re-identification[C]// Proceedings of the 33rd AAAI Conference on Artificial Intelligence, 2019: 8738–8745.
Funds:
National Natural Science Foundation of China (61962046, 61663036, 61841204), Inner Mongolia Jieqing Cultivation Project (2018JQ02), Inner Mongolia Grassland Talents, Inner Mongolia Youth Science and Technology Innovation Talent Project (Level 1), Inner Mongolia Autonomous Region Natural Science Fund (2015MS0604, 2018MS06018), Inner Mongolia Autonomous Region Higher Education Science Funded by the Technical Research Project (NJZY145)
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Zhang Baohua, Zhu Siyu, Lv Xiaoqi, et al. Soft multilabel learning and deep feature fusion for unsupervised person re-identification[J]. Opto-Electronic Engineering, 2020, 47(12): 190636.
