教育管理

刘羽

编辑日期2017-05-04    作者:    阅读次数:989

姓    名刘羽
职    称讲师                                
所属系生物医学工程系
邮    箱yuliu@hfut.edu.cn
电    话
  • 个人简历

    刘羽,博士,讲师,硕士生导师,2007-2016年就读于中国科学技术大学信息科学技术学院,获工学学士学位(20116月)、工学博士学位(20166月),曾获得本科生国家奖学金(20082009)、中国科大第30届郭沫若奖学金(中科大本科生最高荣誉)、硕士生国家奖学金(2013)、博士生国家奖学金(2014)、中国科学院院长优秀奖(2015)、中国科学院朱李月华优秀博士生奖(2016)。20167月进入合肥工业大学仪器科学与光电工程学院任职。目前主要研究方向包括图像处理、计算机视觉、机器学习、信息融合等。已在Information FusionIEEE TIPIEEE SPLIEEE TIMJ-VCIRIET IPCBMMTAPICIPMMSPICIF等国内外知名期刊会议上发表学术论文30余篇,其中以第一/通讯作者身份发表SCI期刊论文10余篇,论文总计被引700余次,多篇次入选ESI热点论文和ESI高被引论文,曾获国际期刊IET Image Processing年度最佳论文奖。主持国家自然科学基金青年基金项目1项、安徽省自然科学基金1项、商汤青年科研基金1项、中央高校基本业务费资助项目3项。担任国家自然科学基金通讯评审专家,担任IEEE TPAMIIEEE TIPIEEE TCIIEEE TMMIEEE TBMEIEEE SPLInformation FusionNeurocomputingIET IP20余个国际知名期刊审稿人。

     

  • 研究领域
    图像处理、计算机视觉、机器学习
  • 开设课程


    本科生:《医学模式识别》、 《医学图像处理》

    研究生:《模式识别》、《图像分析》 
  • 科研项目

    1. 国家自然科学基金青年科学基金项目,61701160,JZ2017GJQN1116,2018.01-2020.12,26.41万元,在研,项目负责人

    2. 安徽省自然科学基金青年基金项目,1808085QF186,JZ2018AKZR0066,2018.07-2020.06,10万元,在研,项目负责人

    3. 商汤青年科研基金,W2018JSKF0481,2018.11-2019.10,20万元,在研,项目负责人

    4. 合肥工业大学学术新人提升计划B项目,JZ2018HGTB0228,2018.05-2019.12,20万元,在研,项目负责人

    5. 合肥工业大学学术新人提升计划A项目,JZ2017HGTA0176,2017.03-2018.12,5万元,结题,项目负责人

    6. 合肥工业大学校博士专项科研资助基金,JZ2016HGBZ1025,2016.10-2018.09,2万元,结题,项目负责人 
  • 发表论文

    一、期刊论文(*表示通讯作者)

    1)一作/通讯:

    [1]. Yu Liu, Chao Zhang, Juan Cheng, Xun Chen, Z. Jane Wang, “A multi-scale data fusion framework for bone age assessment with convolutional neural networks”, Computers in Biology and Medicine, in press, DOI: https://doi.org/10.1016/j.compbiomed.2019.03.015, 2019.

    [2]. Yu Liu, Xun Chen, Rabab Ward, Z. Jane Wang, “Medical image fusion via convolutional sparsity based morphological component analysis”, IEEE Signal Processing Letters, vol. 26, no. 3, pp. 485-489, 2019.

    [3] Ming Yin, Xiaoning Liu, Yu Liu*, Xun Chen, “Medical image fusion with parameter-adaptive pulse coupled neural network in nonsubsampled shearlet transform domain”, IEEE Transactions on Instrumentation and Measurement, vol. 68, no. 1, pp. 49-64, 2019.

    [4] Yu Liu, Xun Chen, Zengfu Wang, Z.Jane Wang, Rabab Ward, X. Wang, “Deep Learning for Pixel-level Image Fusion: Recent Advances and Future Prospects,”Information Fusion, vol. 42, pp. 158-173, 2018.  主编邀稿

    [5] Yu Liu, Xun Chen, Juan Cheng, Hu Peng, Zengfu Wang, “Infrared and visible image fusion with convolutional neural networks”, International Journal of Wavelets, Multiresolution and Information Processing, vol. 16, no. 3, 1850018: 1-20, 2018.

    [6] Yu Liu, Xun Chen, Hu Peng, Zengfu Wang, “Multi-focus image fusion with a deep convolutional neural network”, Information Fusion, vol. 36, pp. 191–207, 2017.  ESI高被引论文

    [7] Yu Liu, Baocai Yin, Jun Yu, Zengfu Wang, “Image classification based on convolutional neural networks with cross-level strategy”, Multimedia Tools and Applications, vol. 76, no. 8, pp. 11065-11079, 2017.

    [8] Yu Liu, Xun Chen, Rabab Ward, Z.Jane Wang, “Image Fusion With Convolutional Sparse Representation”, IEEE Signal Processing Letters, vol. 23, no. 12, pp. 1882-1886, 2016.

    [9] Yu Liu, Shuping Liu, Yang Cao, Zengfu Wang, “Automatic chessboard corner detection method”, IET Image Processing, vol. 10, no. 1, pp. 16-23, 2016.

    [10] Yu Liu, Zengfu Wang, “Dense SIFT for ghost-free multi-exposure fusion”, Journal of Visual Communication and Image Representation, vol. 31, pp. 208-224, 2015.

    [11] Yu Liu, Shuping Liu, Zengfu Wang, “A general framework for image fusion based on multi-scale transform and sparse representation”, Information Fusion, vol. 24, pp. 147-164, 2015.  ESI热点论文、ESI高被引论文、Most cited article published in INFFUS since 2015

    [12] Yu Liu, Zengfu Wang, “Simultaneous image fusion and denoising with adaptive sparse representation”, IET Image Processing, vol. 9, no. 5, pp. 347-357, 2015.  IET Image Processing 2017年度最佳论文,1/230

    [13] Yu Liu, Shuping Liu, Zengfu Wang, “Multi-focus image fusion with dense SIFT”, Information Fusion, vol. 23, pp. 139-155, 2015.  ESI高被引论文

    [14]刘羽, 汪增福. 结合小波变换和自适应分块的多聚焦图像快速融合, 中国图象图形学报, vol. 18, no. 11, pp. 1435-1444, 2013.

      

    2)其他:

    [1] Dingyi Li, Yu Liu, Zengfu Wang, “Video super-resolution using non-simultaneous fully recurrent convolutional network”, IEEE Transactions on Image Processing, vol. 28, no. 3, pp. 1342-1355, 2019.

    [2] Xun Chen, Juan Cheng, Rencheng Song, Yu Liu, Rabab Ward, Z. Jane Wang, “Video-based heart rate measurement: Recent advances and future prospects”, IEEE Transactions on Instrumentation and Measurement, in press, DOI: 10.1109/TIM.2018.2879706, 2018.

    [3] Xuesong Wang, Chen Chen, Yuhu Chen, Xun Chen, Yu Liu, “Zero-shot learning based on deep weighted attribute prediction”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, in press, DOI: 10.1109/TSMC.2018.2837670, 2018.

    [4] Juan Cheng, Fulin Wei, Chang Li, Yu Liu, Aiping Liu, Xun Chen, “Position-independent gesture recognition using sEMG signals via canonical correlation analysis”, Computers in Biology and Medicine, vol. 103, pp. 44-54, 2018.

    [5] Chang Li, Yu Liu, Juan Cheng, Rencheng Song, Hu Peng, Qiang Chen, Xun Chen, “Hyperspectral unmixing with bandwise generalized bilinear model”, Remote Sensing, vol. 2018, no. 10, paper ID 1600, pp. 1-10, 2018.

    [6] Zhengyuan Xu, Yu Liu, Mingquan Ye, Lei Huang, Hao Yu, Xun Chen, “Patch based collaborative representation with Gabor feature and measurement matrix for face recognition”, Mathematical Problems in Engineering, vol. 2018, paper ID 3025264, pp. 1-13, 2018.

    [7] Xun Chen, Aiping Liu, Qiang Chen, Yu Liu, Liang Zou, “Simultaneous Ocular and Muscle Artifact Removal From EEG Data by Exploiting Diverse Statistics”, Computers in Biology and Medicine, vol. 88, pp. 1-10, 2017.

    [8] 刘淑萍, 刘羽, 於俊, 汪增福. 结合手指检测和HOG特征的分层静态手势识别, 中国图象图形学报, vol. 20, no. 6, pp. 781-788, 2015. 《中国图象图形学报》2016年度优秀论文

    [9] 殷保才, 刘羽, 汪增福. 结合色度和纹理不变性的运动阴影检测, 中国图象图形学报, vol. 19, no. 6, pp. 896-905, 2014.

    [10] 向文辉, 刘羽, 曹洋, 汪增福. 基于车载单目图像的3维地平面估计, 机器人, vol. 36, no. 1, pp. 76-82, 2014.

      

    二、会议论文*表示通讯作者)

    [1] Xun Chen, Chao Zhang, Yu Liu*, “Bone Age Assessment with X-Ray Images Based on Contourlet Motivated Deep Convolutional Networks”, 20th IEEE International Workshop on Multimedia Signal Processing (MMSP), Vancouver, Canada, Aug. 29-31, 2018, pp. 1-6.

    [2] Dingyi Li, Yu Liu, Zengfu Wang, “Video super-resolution using motion compensation and residual bidirectional recurrent convolutional network”, 24th IEEE International Conference on Image Processing (ICIP), Beijing, China, Sep. 17-20, 2017, pp. 1642-1646.

    [3] Yu Liu, Xun Chen, Juan Cheng, Hu Peng, “A medical image fusion method based on convolutional neural networks”, 20th International Conference on Information Fusion (ICIF), Xi’an, China, July 10-13, 2017, pp. 1070-1076.

    [4] Yu Liu, Baocai Yin, Jun Yu, Zengfu Wang, “Cross-level: A practical strategy for convolutional neural networks based image classification”, 1st CCF Chinese Conference on Computer Vision (CCCV), Xi’an, China, Sep. 18-20, 2015, CCIS 546, pp. 398-406.

    [5]Yu Liu, Shuping Liu, Yang Cao, Zengfu Wang, “A practical algorithm for automatic chessboard corner detection”, 21thIEEE International Conference on Image Processing (ICIP), Paris, France, Oct. 27-30, 2014, pp. 3394-3398.

    [6]Yu Liu, Shuping Liu, Zengfu Wang, “Medical Image Fusion by combining nonsubsampled contourlet transform and sparse representation”, 6th Chinese Conference on Pattern Recognition (CCPR), Nov. 17-19, Changsha, China, 2014, pp. 372-381.

    [7] Shuping Liu, Yu Liu, Jun Yu, Zengfu Wang, “A static hand gesture recognition algorithm based on Krawtchouk moments”, 6th Chinese Conference on Pattern Recognition (CCPR), Nov. 17-19, Changsha, China, 2014, pp. 321-330.

    [8] Yu Liu, Zengfu Wang, “A practical pan-sharpening method with wavelet transform and sparse representation”, 10th IEEE International Conference on Imaging Systems and Techniques (IST), Beijing, China, Oct. 22-23, 2013, pp. 288-293.

    [9] Yu Liu, Zengfu Wang, “Multi-focus image fusion based on sparse representation with adaptive sparse domain selection”, 7th International Conference on Image and Graphics (ICIG), Qingdao, China, Jul. 26-28, 2013, pp. 591-596.


     
  • 专著教材
     
  • 申请专利

    1. 汪增福, 刘羽. 一种实时的多模态医学图像融合方法. 中国发明专利. 专利授权号:ZL201410427772.3

    2. 刘羽,张超,陈勋,成娟,李畅,宋仁成,基于非下采样轮廓波变换和卷积神经网络的骨龄评估方法,中国发明专利,专利申请号:201810965998.7

    3. 李畅,刘羽,成娟,宋仁成,陈强,彭虎. 一种逐波段广义双线性高光谱图像解混模型和方法. 中国发明专利,专利申请号:201811097454.X

    4. 陈勋,汪旻达,宋仁成,成娟,刘羽. 基于非接触式生理参数测量的跑步机.中国发明专利,专利申请号:201811005513.6

    5. 成娟,陈勋,宋仁成,刘爱萍,刘羽,陈强.一种基于多摄像头的非接触式生命体征参数无缝检测方法. 中国发明专利. 专利申请号:201810269026.4

    6. 陈勋,陶威,李畅,成娟,刘爱萍,刘羽. 噪声环境下基于鲁棒压缩感知的多通道脑电信号重构方法.中国发明专利,专利申请号:201811398547.6

    7. 陈勋,徐雪远,陈强,成娟,刘羽. 一种少数通道的脑电信号中肌电伪迹的消除方法. 专利申请号:201710054115.2 

  • 获奖成果

    国际期刊《IET Image Processing2017年度最佳论文奖

    2017年中国生物医学工程大会青年论文竞赛二等奖
     
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