杜博  

武汉大学计算机学院 人工智能系 人工智能研究所教授 (博导)

研究方向

杜博，武汉大学珞珈特聘教授，国家自然科学基金杰出青年科学基金（2022）、优秀青年科学基金获得者（2018），湖北省杰青（2017）。现任国家多媒体软件工程技术研究中心主任，武汉大学计算机学院院长，武汉大学人工智能研究院常务副院长，多媒体网络通信工程湖北省重点实验室主任。人工智能领域SCI期刊Neural Networks\Pattern Recognition\ Neural Processing Letters的Associate Editor，《中国图象图形学报》编委。主要从事计算机视觉和人工智能等方面的研究工作。近五年主持和参与相关纵向研究课题30余项，发表ESI高引或热点论文23篇，SCI他引8000余次，出版著作3部，授权国家发明专利35项。获得2019年湖北省自然科学一等奖（序1）；2020 IEEE TGRS最佳论文；2020、2021年中国国际高新技术成果交易会优秀产品奖；2020年湖北省首届专利银奖；2019-2021年科睿维安全球高引学者；2020-2022年Elsevier中国高被引学者；人工智能顶会 IJCAI 2018杰出论文奖（CCF A类）；2018 IEEE 全球数据融合大赛冠军；2018 MICCAI全球核磁共振医学图像前列腺分割大赛冠军；2018 医学人工智能顶会MICCAI MLMI Workshop 最佳论文奖；2021 ICCV 多模态视频理解大赛无人机行人重识别赛道冠军；2022全球自然语言处理领域顶级赛事SuperGULE 榜单冠军，2023年国际视频检索技术评测TRECVID年会在跨模态视频检索(AVS)与深度视频理解(DVU)两项任务上同时取得全赛道第一的最好成绩。

主要研究方向为人工智能、数据挖掘、模式识别、计算机视觉和图像处理。具体研究内容包括：

深度学习与图像理解；

迁移学习与分类理论研究；

医学图像处理和癌变细胞诊断；

稀疏学习与目标识别；

图像变化检测；

盲信号分解理论；

推荐系统；

知识图谱与自然语言处理。

热忱欢迎有志向的学子加入我的研究小组，感受技术革新给视觉和图像处理领域带来的日新月异发展，一同在自主创新中完善自我、实现自我！ 本课题组秉承以树人为本的宗旨，以学生的成就为荣，积极引导每位研究生成就属于他们自己的未来，为各位提供优越的科研环境和良好的科研氛围，并努力为各位创造与国际学术权威交流的机会。 欢迎登录我们的团队网址：http://sigma.whu.edu.cn/

教育背景

2005/9 - 2010/7，武汉大学，工学博士
2001/9 - 2005/6，武汉大学，工学学士

工作经验

2015/11 –至今，武汉大学，计算机学院，教授
2014/10 –2015/10，悉尼科技大学，量子计算与智能系统中心，研究员
2013/1 –2015/10，武汉大学，计算机学院，副教授
2012/7 –2012/12，武汉大学，计算机学院，讲师
2010/7 –2012/6，武汉大学，计算机学院，博士后

教授课程

《物联网定位技术》、《研究生前沿课程》、《面向对象程序设计》、《多媒体技术》

发表论文

近年小组论文详列：

[1] B. Du，X. Tang, L. Zhang etc. "Robust Graph-based Semi-supervised Learning for Noisy Labeled Data via Maximum Correntropy Criterion," IEEE Transactions on Cybernetics, DOI (identifier) 10.1109/TCYB.2018.2804326, 2018.
[2] B. Du, Z. Huang, N. Wang, L. Zhang, “A Band-wise Noise Model combined with Low-rank Matrix Factorization for Hyperspectral Image Denoising,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, DOI (identifier) 10.1109/JSTARS.2018.2805290, 2018.
[3] B. Du，Z. Wang, etc. “Robust and Discriminative Labeling for Multi-label Active Learning Based on Maximum Correntropy Criterion,” IEEE Transactions on Image Processing, vol. 26, no. 4, 1694-1707, 2017.
[4] B. Du，Y. Zhang, etc. "Beyond the Sparsity-Based Target Detector: A Hybrid Sparsity and Statistics Based Detector for Hyperspectral Images," IEEE Transactions on Image Processing, vol. 25, no. 11, pp. 5345-5357, 2016.
[5] B. Du，M. Zhang, etc. “PLTD: Patch-Based Low-Rank Tensor Decomposition for Hyperspectral Images,” IEEE Transactions on Multimedia, vol. 19, no. 1, pp. 67-79, Jan 2017.
[6] B. Du, X. Xiong, L. Zhang, etc., “Stacked Convolutional Denoising Auto-Encoders for Feature Representation,” IEEE Transactions on Cybernetics, vol. 47, no. 4, pp.1017-1027 Apr, 2017.
[7] B. Du, Z. Wang, L. Zhang, etc., “Exploring Representativeness and Informativeness for Active Learning,” IEEE Transactions on Cybernetics, vol.47, no. 1, pp. 14-26, 2017.
[8] B. Du and L. Zhang*, "A discriminative metric learning based anomaly detection method," IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 11, pp. 6844-6857, Nov 2014.
[9] B. Du* and L. Zhang, "Target detection based on a dynamic subspace," Pattern Recognition, vol. 47, no. 1, pp. 344-358, Jan 2014.
[10] B. Du, L. Zhang*, “Random-Selection-Based Anomaly Detector for Hyperspectral Imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 5, pp. 1578–1589, May, 2011.
[11] B. Du, Z. Huang, N. Wang, Y. Zhang, X. Jia, “Joint weighted nuclear norm and total variation regularization for hyperspectral image denoising,” International Journal of Remote Sensing, vol. 39, no. 2, pp. 334-355, 2018.
[12] B. Du, Y. Sun, S. Cai, C. Wu, and Q. Du, “Object Tracking in Satellite Videos by Fusing the Kernel Correlation Filter and the Three-Frame-Difference Algorithm,” IEEE Geoscience and Remote Sensing Letters, vol. 15, no.2, pp. 168-172, 2018.
[13] B. Du, S. Wang, N. Wang*, et al., “Hyperspectral signal unmixing based on constrained non-negative matrix factorization approach,”Neurocomputing, vol. 204, pp. 153–161, September 2016.
[14] B. Du, R. Zhao, L. Zhang, et al., “A spectral-spatial based local summation anomaly detection method for hyperspectral images,”Signal Processing, vol. 124, pp. 115–131 July 2016.
[15] B. Du, Y. Zhang, and L. Zhang*, “A hypothesis independent subpixel target detector for hyperspectral Images,” Signal Processing, vol. 110, pp. 244-249, May, 2015.
[16] B. Du*, L. Zhang, D. Tao, and D. Zhang, "Unsupervised transfer learning for target detection from hyperspectral images,"Neurocomputing, vol. 120, pp. 72-82, Nov 2013.
[17] B. Du*, L. Zhang, L. Zhang, T. Chen, K. Wu, “A Discriminative Manifold Learning Based Dimension Reduction Method for Hyperspectral Classification,” International Journal of Fuzzy Systems, vol. 14, no. 2, pp, 272-277, Jun. 2012.
[18] Q. Shi, B. Du*, and L. Zhang, “Spatial Coherence Based Batch-Mode Active Learning for Remote Sensing Images Classification,” IEEE Transactions on Image Processing, vol. 24, no. 7, pp. 2037-2050, July, 2015.
[19] Z. Wang, B. Du*, L. Zhang, L. Zhang, and X. Jia, “A Novel Semi-Supervised Active Learning Algorithm for Hyperspectral Image Classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no.6, pp. 3071-3083, 2017.
[20] W. Xiong, B. Du*, etc., “Combining Local and Global: Rich and Robust Feature Pooling for Visual Recognition,” Pattern Recognition, vol. 62, pp. 225-235, February 2017.
[21] L. Zhang, X. Zhu, L. Zhang, and B. Du*, "Multidomain Subspace Classification for Hyperspectral Images," IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 10, pp. 6138-6150, Oct. 2016.
[22] Y. Zhang, B. Du*, T. Liu, and L. Zhang, “Joint Sparse Representation and Multi-Task Learning for Hyperspectral Target Detection,”IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 2, pp. 894 - 906, 2017.
[23] R. Zhao, B. Du*, L. Zhang, “A Robust Background Regression Based Score Estimation Algorithm for Hyperspectral Anomaly Detection,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 122, pp. 126-144, 2016.
[24] L. Zhang, Q. Zhang, B. Du*, ect., “Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images,” IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2016.2605044, 2016.
[25] N. Zhao, L. Zhang, B. Du*, Q. Zhang, J. You and D. Tao, “Robust Dual Clustering with Adaptive Manifold Regularization,” IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 11, pp. 2498-2509, 2017.
[26] H Wang, W Hu, Z Qiu, and B Du*, “Nodes' evolution diversity and link prediction in social networks,” IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 10, pp. 2263-2274, 2017.
[27] R. Liu, B. Du*, and L. Zhang, “Hyperspectral Unmixing via Double Abundance Characteristics Constraints Based NMF,” Remote Sensing, vol. 8, no. 6, DOI: 10.3390/rs8060464, 2016.
[28] L Zhang, Q Zhang, L Zhang, D Tao, X Huang, and B Du*, “Ensemble Manifold Regularized Sparse Low-Rank Approximation for Multiview Feature Embedding,” Pattern Recognition, vol. 48, no. 10, pp. 3102-3112, Oct. 2015.
[29] Y. Zhang, W. Ke, B. Du*, X Hu, “Independent Encoding Joint Sparse Representation and Multitask Learning for Hyperspectral Target Detection,” IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 11, pp. 1933-1937, 2017.
[30] Z. Wang, B. Du*, L. Zhang, and L. Zhang, “A batch-mode active learning framework by querying discriminative and representative samples for hyperspectral image classification”, Neurocomputing, vol. 179, pp. 88-100,  February 2016.
[31] L. Zhang, L. Zhang, D. Tao, X. Huang, B. Du*, “Compression of hyperspectral remote sensing images by tensor approach,”Neurocomputing, vol. 147, pp. 358-363, Jan 2015.
[32] C. Wu, L. Zhang, and B. Du*, "Hyperspectral anomaly change detection with slow feature analysis," Neurocomputing, vol. 151, Part 1, pp. 175-187, Mar. 2015.
[33] L. Zhang, L. Zhang, D. Tao, B. Du*, “A sparse and discriminative tensor to vector projection for human gait feature representation,”Signal Processing, vol. 106, pp. 245–252, Jan 2015.
[34] Y. Dong, L. Zhang, L. Zhang, B. Du*, “Maximum margin metric learning based target detection for hyperspectral images,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 108, pp. 138–150, Oct 2015.
[35] R. Liu, B. Du*, L. Zhang, "Endmember number estimation for hyperspectral imagery based on vertex component analysis," Journal of Applied Remote Sensing，vol. 8, no. 1, 085093, vol. 8, no. 1, pp. 085093-085093, Sep 2014.
[36] W Li, L Zhang, L Zhang, B Du*, “GPU parallel implementation of isometric mapping for hyperspectral classification,” IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 9, pp. 1532-1536, 2017.
[38] Y. Dong, B. Du*, L. Zhang, L. Zhang, and D. Tao, “LAM3L: Locally Adaptive Maximum Margin Metric Learning for Visual Data Classification,” Neurocomputing, vol. 235, no. 26, pp. 1-9, 2017.
[39] Y. Zhang, K. Wu, B. Du*, L. Zhang, and X Hu, “Hyperspectral Target Detection via Adaptive Joint Sparse Representation and Multi-Task Learning with Locality Information,” Remote Sensing, vol. 9, no. 5, pp. 482-492, 2017.
[40] M. Xu, L. Zhang, B. Du*, L. Zhang, Y. Fan, and D Song, “A mutation operator accelerated quantum-behaved particle swarm optimization algorithm for hyperspectral endmember extraction,” Remote Sensing, vol. 9, no. 3, pp. 197-208, 2017.
[41] C. Wu, L. Zhang* and B. Du, “Kernel Slow Feature Analysis for Scene Change Detection,” IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 4, pp. 2367-2384, 2017.
[43] R. Liu, L. Zhang* and B. Du, “A Novel Endmember Extraction Method for Hyperspectral Imagery Based on Quantum-Behaved Particle Swarm Optimization,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, DOI:10.1109/JSTARS.2016.2640274.
[44] R. Zhao, B. Du, L. Zhang*, “Hyperspectral Anomaly Detection via A Sparsity Score Estimation Framework”, IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 6, pp. 3208-3222, 2017.
[45] Y. Dong, B. Du, L. Zhang, and L. Zhang*, “Dimensionality Reduction and Classification of Hyperspectral Images Using Ensemble Discriminative Local Metric Learning,” IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 2, pp. 2509-2524, 2017.
[46] S. Chang, B. Du, L. Zhang*, and R. Zhao, "IBRS: An Iterative Background Reconstruction and Suppression Framework for Hyperspectral Target Detection," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., DOI (identifier) 10.1109/JSTARS.2017.2676120.
[47] X. Li, L. Zhang, B. Du, L. Zhang*, and Q. Shi, "An Iterative Reweighting Heterogeneous Transfer Learning Framework for Supervised Remote Sensing Image Classification," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., DOI: 10.1109/JSTARS.2016.2646138, 2017.
[48] L. Zhang*, B. Du, and Y. Zhong, "Hybrid Detectors Based on Selective Endmembers," IEEE Transactions on Geoscience and Remote Sensing, vol. 48, no. 6, pp. 2633-2646, June, 2010.
[49] Y. Dong, B. Du, and L. Zhang*, “Target Detection Based on Random Forest Metric Learning,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 4, pp. 1830-1838, April, 2015.
[50] N. Wang, B. Du, L. Zhang*, “An Abundance Characteristic Based Independent Component Analysis for Hyperspectral Unmxing,” IEEE Trans. on Geoscience and Remote Sensing, vol. 53, no. 1, pp. 416-428, Jan. 2015.
[51] Y. Zhang, B. Du, and L. Zhang*, “A Sparse Representation-Based Binary Hypothesis Model for Target Detection in Hyperspectral Images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 3, pp. 1346-1354, March, 2015.

Conference Papers
[1] R. Zhao, X. Han, B. Du, L. Zhang, “Sparsity Score Estimation for Hyperspectral Anomaly Detection,” Proceedings of the 4th IEEE/IIAE International Conference on Intelligent Systems and Image Processing 2016(ICISIP). (Best Paper Award)
[2] Z. Wang, B. Du, etc., “On Gleaning Knowledge from Multiple Domains for Active Learning”, 26th International Joint Conference on Artificial Intelligence (IJCAI 2017).
[3] L. Zhang, Q. Zhang, B. Du, etc., “Adaptive Manifold Regularized Matrix Factorization for Data Clustering” 26th International Joint Conference on Artificial Intelligence (IJCAi 2017).
[4] L. Zhang, Q. Zhang, B. Du*, ect., “Robust manifold matrix factorization for joint clustering and feature extraction,” the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17).
[5] Z Wang, B Du*, L Zhang, L Zhang, M Fang, D Tao, “Multi-label Active Learning Based on Maximum Correntropy Criterion: Towards Robust and Discriminative Labeling,” 2016 European Conference on Computer Vision(ECCV), pp. 453-468.
[6] W. Xiong, B. Du*, L Zhang, et al., “SCDAE: Stacked Convolutional Denoising Autoencoders towards Robust Unsuperived Feature Representation,” 2016 The Annual International Joint Conference on Neural Networks(IJCNN).
[7] N. Zhao, L. Zhang and B. Du*, “Sparse Tensor Discriminative Locality Alignment for Gait Recognition,” 2016 The Annual International Joint Conference on Neural Networks(IJCNN).
[8] W. Xiong, B. Du*, L Zhang, et al., “Regularizing Deep Convolutional Neural Networks with a Structured Decorrelation  Constraint”2016 IEEE International Conference on Data Mining (ICDM).
[9] W. Xiong, B. Du*, L Zhang, et al., “R2FP: rich and robust feature pooling for mining visual data,” 2015 IEEE International Conference on Data Mining (ICDM), pp. 469-478.
[10] Q. Zhang, L. Zhang*, B. Du, et al., “MMFE: Multitask Multiview Feature Embedding,” 2015 IEEE International Conference on Data Mining (ICDM), pp. 1105-1110.
[11] B. Du, M. Zhang, L. Zhang*, X. Li, "Hyperspectral biological images compression based on multiway tensor projection," in 2014 IEEE International Conference on Multimedia and Expo, (ICME).
[12] B. Du, N. Wang*, and D. Tao, “A Spectral Dissimilarity Constrained Nonnegative Matrix factorization based Cancer Screening Algorithm from Hyperspectral Fluorescence Images” in 2012 International Conference on Computerized Healthcare (ICCH 2012), 2012.
[13] B. Du*, N. Wang, and D. Tao, “Hyperspectral medical images unmixing for cancer screening based on rotational independent component analysis,” in International Conference on Intelligence Science and Big Data Engineering (IScIDE 2013), 2013.
[14] B. Du, L. Zhang*, and L. Zhang, “A Manifold Learning based Feature Extraction Method for Hyperspectral Classification,” in Second International Conference on Information Science and Technology, vol. ISSU, pp. 491-494, 2012.
[15] B. Du*, D. Zhang, P.  Li, T.  Chen, and K. Wu, “A structured sub-pixel target detection method base on manifold learning method,”Proc. SPIE 8002, MIPPR 2011: Multispectral Image Acquisition, Processing, and Analysis, pp. 80020N, December 2011.
[16] B. Du*, K. Wu, L. Zhang, and T. Chen, “An Unstructured Sub-pixel Target Detector for Hyperspectral Imagery,” in 2010 International Conference on Computer Application and System Modeling vol. 11 ISSU, pp.V11469-V11472, 2010.
[17] B. Du*, and L. Zhang, “Robust Metric based Anomaly Detection in Kernel Feature Space,” in XXII Congress of the International Society for Photogrammetry and Remote Sensing (ISPRS 2012), vol. XXXIX-B7, 2012.
[18] Q. Shi, B. Du* and L. Zhang , “An novel active learning strategy for hyperspectral image classification,” in 4th IEEE GRSS Workshop on Hyperspectral Image and Signal Processing: evolution in remote sensing (WHISPERS 2012).
[19] C. Wu, L. Zhang and B. Du*, “Targeted Change Detection For Stacked Multi-Temporal Hyperspectral Image,” in 4th IEEE GRSS Workshop on Hyperspectral Image and Signal Processing: evolution in remote sensing (WHISPERS 2012).
[20] N. Wang, L. Zhang and B. Du*, “An Endmember Dissimilarity Based Non-negative Matrix Factorization Method for Hyperspectral Unmixing,” in 4th IEEE GRSS Workshop on Hyperspectral Image and Signal Processing: evolution in remote sensing (WHISPERS 2012).
[21] M. Xu, L. Zhang and B. Du*, “An endmember extraction framework based on abundance constraint,” in 4th IEEE GRSS Workshop on Hyperspectral Image and Signal Processing: evolution in remote sensing (WHISPERS 2012).
专利情况：
一种高光谱遥感影像异常探测方法（专利号：201010130302，专利已经成功转让。）
一种慢特征分析的高光谱影像变化检测方法（专利号：201310689353.2）
一种高光谱图像混合像元分解算法（专利号：201610206981.4）
一种基于量子粒子群算法的高光谱图像端元提取算法（专利号：201610205990.1）
一种基于量子优化的高光谱遥感影像端元提取方法（专利号：201610157103.8）
一种基于非局部和低秩分解的高光谱图像压缩方法（专利号：201610157079.8）
一种高光谱遥感影像目标探测方法（专利号：201610156117.8）
一种高光谱遥感影像端元提取方法（专利号：201610156222.1）
基于稳健背景回归的高光谱遥感影像异常目标探测方法（专利号：201610156116.3）
一种基于图构造的高光谱遥感影像异常目标探测方法（专利号：201610156118.2）

科研课题

教师：
张乐飞  教授
张玉香  副教授

董燕妮  副教授

武辰  副教授

罗甫林  副教授

王增茂  副教授

研究生：

李雪  2015级硕士生（1+4博士连续）
朱其奎 2015级硕士生 (2+3硕博连读)

.......

钟永建2016级保送生（物理学基地班）
宋俍辰2016级保送生（数学基地班）
徐永浩2016级保送生（1+4博士连续）
张祎铭2016级保送生
常世桢2016级保送生（1+4博士连续）
王勇2016级保送生
肖攀2016级保送生

......

本科生：
蔡诗晗 保送生
夏海峰 保送生
曾梓龙
普佳萌（出国读博）
毛凤玲（保送上海科大）

毕业生：
王挺 2014届博士毕业生：香港中文大学 助理研究员，博士后
王楠 2014届博士毕业生：中国科学院遥感与数字地球研究所 助理研究员
石茜 2015届博士毕业生：中山大学，副教授

许明明2016届博士毕业生：中国石油大学，博士后，讲师

熊绍龙2016届硕士研究生：广州中科沃土金融公司
林昱坤2016届硕士毕业生：中科院遥感与数字地球研究所 攻读博士

董燕妮2017届博士毕业生：中国地质大学（武汉）特任副教授

张帆2017届博士毕业生：    阿里巴巴AI实验室

赵锐2017届博士毕业生：    美团研究院

王少东2017届硕士毕业生：华为技术公司（杭州）

朱晓杰2017届硕士毕业生：通甲优博公司

李万2017届硕士毕业生：华为技术公司（上海）

孙雨佳2017届硕士毕业生：华为技术公司（深圳）

熊维2017届硕士毕业生：    美国罗切斯特理工大学 攻读博士

章梦飞2017届硕士毕业生： 深圳证券交易所

刘蓉   2018届博士毕业生： 德国宇航中心 博士后

黄志强 2018届硕士毕业生：数字政通科技公司

唐新瑶 2018届硕士毕业生：兴业银行（上海）

知识产权

2018年 人工智能顶会IJCAI的Distinguished Paper

2018年  IEEE 全球数据融合大赛总冠军

2018年 International Conference on Medical Image Computing and Computer Assisted Intervention(MAICCAI) 全球核磁共振医学图像前列腺分割大赛（PROMISE12）总冠军

2016年 IEEE International Conference on Intelligent Systems and Image Processing 2016(ICISIP) Best Paper Award

2015年 国际计算机学会学术新星奖

2015年 IEEE Senior Member

2015年 湖北省自然科学优秀论文奖

2014年 The 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems国际大会的分会场主席

2013年 武汉大学“珞珈青年学者”

2013年 湖北省自然科学优秀论文奖

2012年 担任 IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution and Remote Sensing 的 Session Chair (“IEEE 分会场主席”)

2011年 IEEE BEST REVIEWER (“IEEE 最佳审稿人”)

2010年 武汉大学优秀博士科研成果展

学术服务

· The Association for the Advance of Artificial Intelligence 2017\2018国际会议，高级程序委员会委员
· International Conference on Pattern Recognition 2018, 区域主席
· The IEEE International Geoscience and Remote Sensing Symposium 2016国际会议，分会主席
· The 11th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application (VISIGRAPP 2016)国际会议，程序委员会委员
· 2015 International Conference on Fuzzy System and Data Mining（FSDM2015）国际会议，组委会成员
· The 19th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES 2015)国际会议，组委会成员
· The 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES 2014)国际会议，分会主席
· IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS 2014)国际会议，组委会成员
· IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS 2012)国际会议，分会主席
· 图像处理领域权威期刊“IEEE Trans. Image Process”、 “IEEE Trans. Signal Process” 、“IEEE Trans. Geosci. Remote Sens.”等近20个国际学术刊物和《计算机学报》、《软件学报》、《中国图象图形学报》、《光子学报》等10个国内核心学术期刊审稿人

成果展示

其他

2018.7.20   课题组论文荣获CCF A类会议IJCAI的杰出论文奖

2018.6.10   课题组荣获(MAICCAI) 全球核磁共振医学图像前列腺分割大赛（PROMISE12）总冠军

2018.5.20   课题组荣获IEEE全球数据融合大赛总冠军

2018.3.29   武汉大学计算机学院人工智能樱花论坛顺利举行

2018.2.14   课题组宋俍辰同学参加 AAAI 2018

2017.12.28  杜博教授获评武汉大学第八届“我心目中的好导师”荣誉称号

2017.11.10  课题组一篇论文入选 ESI 高被引论文

2017.10.14  小组研究生参加 CCCV 2017

2017.9.5   小组研究生参加第四届全国成像光谱技术与应用研讨会

2017.8.25   课题组王增茂博士研究生参加 IJCAI

2017.7.23   课题组博士研究生参加 IGARSS 并做分组

2017.7.20   课题组一篇论文被 CCF A 类期刊 IEEE TKDE 录用

2017.6.30   课题组一篇论文被 IEEE GRSL 录用

2017.5.1   课题组两篇论文被 CCF A 类会议 IJCAI 录用