副教授
硕士生导师
入职时间：2018-05-14
所在单位：计算机学院
学历：博士研究生毕业
办公地点：中南大学校本部计算机楼
性别：女
联系方式：sliu35@csu.edu.cn
学位：博士学位
在职信息：在职
学科：计算机科学与技术
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最后更新时间：..
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[21]W. Yang, B. Zou, K. Li, S. Liu*（刘姝）.A character flow framework for multi-oriented scene text detection[J].Journal of Computer Science and Technology, 2021, 36 (3) : 465-477.
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[22]B. Zou, T. Lei, S. Liu*（刘姝）, W. Liao, L. Jiang.Construction of vehicle logo dataset in natural scenes and its application[J].Journal of National University of Defense Technology, 2021, 43 (1) : 95-102 (in Chinese).
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[23]B. Zou, W. Yang, S. Liu*（刘姝）, L. Jiang.A three-stage text recognition framework for natural scene images[J].Journal of Zhejiang University (Science Edition), 2021, 48 (1) : 1-8 (in Chinese).
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[24]S. Liao#, C. Xiong#, S. Liu*（刘姝）, Y. Zhang, C. Peng.3D object reassembly using region-pair-relation and balanced cluster tree[J].Computer Methods and Programs in Biomedicine, 2020, 197: 105756.
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[25]S. Liu（刘姝）, J. He, S. Liao*.Automatic detection of anatomical landmarks on geometric mesh data[C].ICME, 2020: 1-6.
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[26]Y.-Y. Fan, S. Liu*（刘姝）, B. Li, Z. Guo, A. Samal, J. Wan, S. Z. Li.Label distribution-based facial attractiveness computation by deep residual learning[J].IEEE Transactions on Multimedia, 2018, 20 (8) : 2196-2208.
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[27]S. Liu#（刘姝）, B. Li#, Y.-Y. Fan, Z. Guo, A. Samal.Facial attractiveness computation by label distribution learning with deep CNN and geometric features[C].ICME, 2017: 1344-1349.
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[28]S. Liu（刘姝）, Y.-Y. Fan, Z. Guo, A. Samal, A. Ali.A landmark-based data-driven approach on 2.5D facial attractiveness computation[J].Neurocomputing, 2017, 238: 168-178.
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[29]S. Liu（刘姝）, Y.-Y. Fan, A. Samal, Z. Guo.Advances in computational facial attractiveness methods[J].Multimedia Tools and Applications, 2016, 75 (23) : 16633-16663.
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[30]Z. Guo, S. Liu（刘姝）, Y. Wang, T. Lei.Learning deformation model for expression-robust 3D face recognition[C].ICGIP, 2015: 98170O1-98170O6.
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[31]S. Liu（刘姝）, Y. Fan, Z. Guo, A. Samal.2.5D facial attractiveness computation based on data-driven geometric ratios[C].IScIDE, 2015: 564-573.
