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个人简介:

周武杰,1983年9月出生,副教授/博士后,硕士生导师,浙江省电子学会理事,Member IEEE, Member CCF,中国人工智能学会会员,浙江省“计算机科学与技术”一流学科B类方向负责人。2012年入选“青年骨干教师”,2015年入选“优秀青年教师资助计划”,2016年入选“科大青年英才”。浙江大学信息与通信工程专业博士后,国家留学基金委公派新加坡南洋理工大学访问学者1年。主要从事人工智能与深度学习、机器视觉与模式识别、图像处理等方面的研究;近几年以第一作者在《IEEE Transactions on Image Processing》《IEEE Transactions on Circuits and Systems for Video Technology》《IEEE Transactions on Multimedia》《IEEE Transactions on Computational Imaging》《IEEE Transactions on Systems, Man, and Cybernetics: Systems》《IEEE Transactions on Broadcasting》《IEEE Intelligent Systems》《IEEE Transactions on Cognitive and Developmental Systems》《IEEE Transactions on Emerging Topics in Computational Intelligence》《Pattern Recognition》等国际权威SCI期刊或核心期刊上发表学术论文50多篇,其中SCI收录30多篇(中科院一区10篇, CAA-A类期刊9篇, CCF-A和B类期刊8篇,ESI热点/高被引论文1篇),H指数 (h-index)17 (Google Scholar),被引频次总计800+ (Google Scholar);申请国家发明专利60多项,授权40多项,多项已转让投产;获市科学技术进步奖二等奖1项;担任国家基金通讯评审专家,浙江省科技专家库专家,广东省基金项目评审专家;担任IEEE TIP、IEEE TCYB、IEEE TMM、IEEE TBC、IEEE JSTSP、IEEE SMC-S、IEEE SPL等国外权威SCI期刊稿件评审人。目前,主持国家自然科学基金1项,省自然科学基金2项,中国博士后基金1项,企业重大横向课题3项,重中之重实验室开放基金2项和教育厅科研项目1项。指导学生获中国服务外包创新创业大赛二等奖1项。

招收研究生:

视觉智能感知与理解实验室(中央支持地方高校改革发展专项资助建设,项目编号:303011-2019-0008)招收硕士研究生(学硕:先进制造与信息化,专硕:机械、应用统计),主要研究方向:人工智能与深度学习、机器视觉与模式识别、图像处理、视觉大数据统计与应用。本实验室毕业生起薪15K/月以上,目前指导的研究生中4名获国家奖学金(奖金2万/人),1名获卓越学子奖学金(奖金3万/人)。预加入实验室请发个人简历和本科成绩(可系统截图)到E-mail: wujiezhou@163.com

代表作

W. Zhou*, L. Yu, Y. Zhou, W. Qiu, M. Wu and T. Luo, “Local and Global Feature Learning for Blind Quality Evaluation of Screen Content and Natural Scene Images,”IEEE Transactions on Image Processing, vol. 27, no. 5, pp. 2086-2095, May 2018, doi: 10.1109/TIP.2018.2794207. (SCI)

W. Zhou*, J. Wu, J. Lei, J.-N. Hwang and L. Yu, “Salient Object Detection in Stereoscopic 3D Images Using a Deep Convolutional Residual Autoencoder,”IEEE Transactions on Multimedia, doi: 10.1109/TMM.2020.3025166(SCI)

W. Zhou*, Q. Guo, J. Lei, L. Yu and J.-N. Hwang, “ECFFNet: Effective and Consistent Feature Fusion Network for RGB-T Salient Object Detection,”IEEE Transactions on Circuits and Systems for Video Technology, doi: 10.1109/TCSVT.2021.3077058(SCI)

W. Zhou*, Y. Zhu, J. Lei, J. Wan, and L. Yu, CCAFNet: Crossflow and cross-scale adaptive fusion network for detecting salient objects in RGB-D images, IEEE Transactions on Multimedia, doi: 10.1109/TMM.2021.3077767(SCI)

W. Zhou*, Y. Lv, J. Lei and L. Yu, “Global and Local-Contrast Guides Content-Aware Fusion for RGB-D Saliency Prediction,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, doi: 10.1109/TSMC.2019.2957386. (SCI)

W. Zhou*, X. Lin, J. Lei, L. Yu and J.-N. Hwang, “MFFENet: Multiscale Feature Fusion and Enhancement Network for RGB–Thermal Urban Road Scene Parsing,” IEEE Transactions on Multimedia, doi: 10.1109/TMM.2021.3086618.(SCI)

W. Zhou* and L. Yu, “Binocular Responses for No-Reference 3D Image Quality Assessment,” IEEE Transactions on Multimedia, vol. 18, no. 6, pp. 1077-1084, June 2016, doi: 10.1109/TMM.2016.2542580. (SCI)

W. Zhou*, J. Lei, Q. Jiang, L. Yu and T. Luo, “Blind Binocular Visual Quality Predictor Using Deep Fusion Network,” IEEE Transactions on Computational Imaging, vol. 6, pp. 883-893, 2020, doi: 10.1109/TCI.2020.2993640. (SCI)

W. Zhou*, J. Lei, T. Luo, “TSNet: Three-stream Self-attention Network for RGB-D Indoor Semantic Segmentation,” IEEE Intelligent Systems, doi: 10.1109/MIS.2020.2999462 (SCI)

W. Zhou*, W. Qiu and M. Wu, “Utilizing Dictionary Learning and Machine Learning for Blind Quality Assessment of 3-D Images,”IEEE Transactions on Broadcasting, vol. 63, no. 2, pp. 404-415, June 2017, doi: 10.1109/TBC.2016.2638620. (SCI)

W. Zhou*, W. Liu, J. Lei, T. Luo, L. Yu, Deep binocular fixation prediction using hierarchical multimodal fusion network, IEEE Transactions on Cognitive and Developmental Systems, Accept, doi: 10.1109/TCDS.2021.3051010(SCI)

W. Zhou*, S. Pan, J. Lei, and L. Yu, "TMFNet: Three-Input Multilevel Fusion Network for Detecting Salient Objects in RGB-D Images", IEEE Transactions on Emerging Topics in Computational Intelligence, DOI: 10.1109/TETCI.2021.3097393(SCI)

W. Zhou*, L. Yu, Y. Zhou, W. Qiu, M.-W. Wu, T. Luo, “Blind quality estimator for 3D images based on binocular combination and extreme learning machine,” Pattern Recognition, vol. 71, pp. 207–217, Nov. 2017. (SCI)

2021年

W. Zhou*, S. Pan, J. Lei, and L. Yu, "TMFNet: Three-Input Multilevel Fusion Network for Detecting Salient Objects in RGB-D Images", IEEE Transactions on Emerging Topics in Computational Intelligence, DOI: 10.1109/TETCI.2021.3097393(SCI)

W. Zhou*, X. Lin, J. Lei, L. Yu and J.-N. Hwang, "MFFENet: Multiscale Feature Fusion and Enhancement Network for RGB–Thermal Urban Road Scene Parsing,”IEEE Transactions on Multimedia, doi: 10.1109/TMM.2021.3086618.(SCI)

W. Zhou*, Y. Zhu, J. Lei, J. Wan, and L. Yu, CCAFNet: Crossflow and cross-scale adaptive fusion network for detecting salient objects in RGB-D images, IEEE Transactions on Multimedia, doi: 10.1109/TMM.2021.3077767(SCI)

W. Zhou*, Q. Guo, J. Lei, L. Yu and J.-N. Hwang, “ECFFNet: ECFFNet: Effective and Consistent Feature Fusion Network for RGB-T Salient Object Detection,” IEEE Transactions on Circuits and Systems for Video Technology, doi: 10.1109/TCSVT.2021.3077058(SCI)

W. Zhou*, W. Liu, J. Lei, T. Luo, L. Yu, Deep binocular fixation prediction using hierarchical multimodal fusion network, IEEE Transactions on Cognitive and Developmental Systems, Accept, doi: 10.1109/TCDS.2021.3051010(SCI)

W. Zhou*, S. Pan, J. Lei, T. L. Yu, Multi-level Reverse Context Interactive Fusion Network for RGB-D Salient Object Detection, IEEE Signal Processing Letters, doi: 10.1109/LSP.2021.3092967(SCI)

W. Zhou*, Y. Chen, J. Lei, L. Yu, X. Zhou, T. Luo, Boundary-aware pyramid attention network for detecting salient objects in RGB-D images, Digital Signal Processing, https://doi.org/10.1016/j.dsp.2021.102975(SCI)

Y. Zhu (研究生), W. Zhou*, Q. Li, L. Yu, Parallax Estimation Enhance Network for binocular salient object detection, IEEE Signal Processing Letters, doi: 10.1109/LSP.2021.3075610(SCI)

Y. Yue (研究生), W. Zhou*, J. Lei, L. Yu, Two-Stage Cascaded Decoder for Semantic Segmentation of RGB-D Images, IEEE Signal Processing Letters, doi: 10.1109/LSP.2021.3084855(SCI)

K. Huang (本科生) and W. Zhou*, “Saliency Prediction of S3D images,”Computational Intelligence and Neuroscience, https://doi.org/10.1155/2021/8841681(SCI)

2020年

W. Zhou*, J. Wu, J. Lei, J.-N. Hwang and L. Yu, “Salient Object Detection in Stereoscopic 3D Images Using a Deep Convolutional Residual Autoencoder,” IEEE Transactions on Multimedia, doi: 10.1109/TMM.2020.3025166(SCI)

W. Zhou*, Y. Lv, J. Lei and L. Yu, “Global and Local-Contrast Guides Content-Aware Fusion for RGB-D Saliency Prediction,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, doi: 10.1109/TSMC.2019.2957386. (SCI)

W. Zhou*, J. Lei, Q. Jiang, L. Yu and T. Luo, “Blind Binocular Visual Quality Predictor Using Deep Fusion Network,” IEEE Transactions on Computational Imaging, vol. 6, pp. 883-893, 2020, doi: 10.1109/TCI.2020.2993640.(SCI)

W. Zhou*, J. Lei, T. Luo, “TSNet: Three-stream Self-attention Network for RGB-D Indoor Semantic Segmentation,” IEEE Intelligent Systems, doi: 10.1109/MIS.2020.2999462 (SCI)

W. Zhou*, Y. Chen, C. Liu and L. Yu, “GFNet: Gate Fusion Network with Res2Net for Detecting Salient Objects in RGB-D Images,” IEEE Signal Processing Letters, doi: 10.1109/LSP.2020.2993471. (SCI)

W. Zhou*, X. Lin, X. Zhou, J. Lei, L. Yu, and T. Luo, “Multi-layer fusion network for blind stereoscopic 3D visual quality prediction,” Signal Processing: Image Communication, https://doi.org/10.1016/j.image.2020.116095. (SCI)

W. Zhou*, S. Pan, J. Lei, L. Yu, X. Zhou and T. Luo, “Three-branch architecture for stereoscopic 3D salient object detection,” Digital Signal Processing, https://doi.org/10.1016/j.dsp.2020.102818 (SCI)

W. Zhou*, X. Lin, J. Lei, L. Yu, X. Zhou and T. Luo, “Opinion-Unaware Blind Picture Quality Measurement Using Deep Encoder-Decoder Architecture,” Digital Signal Processing, https://doi.org/10.1016/j.dsp.2020.102834(SCI)

J. Wu (研究生), W. Zhou*, T. Luo, L. Yu, J. Lei, “Multiscale Multilevel Context and Multimodal Fusion for RGB-D Salient Object Detection,” Signal Processing, Vol. 178, 2021, 107766. doi: 10.1016/j.sigpro.2020.107766(SCI, ESI高被引论文

C. Liu (研究生), W. Zhou*, Y. Chen, J. Lei, “Asymmetric Deeply Fused Network for Detecting Salient Objects in RGB-D Images,” IEEE Signal Processing Letters, doi: 10.1109/LSP.2020.3023349(SCI)

W. Liu (研究生), W. Zhou*, T. Luo, “Cross-Modal Feature Integration Network for Human Eye-Fixation Prediction in RGB-D Images,” IEEE Access, doi: 10.1109/ACCESS.2020.3036681(SCI)

Y. Lv (研究生), W. Zhou*, “Hierarchical multimodal adaptive fusion (HMAF) network for prediction of RGB-D saliency,” Computational Intelligence and Neuroscience, https://doi.org/10.1155/2020/8841681(SCI)

Y. Chen (研究生), W. Zhou*, “Hybrid-Attention Network for RGB-D Salient Object Detection,” Appl. Sci. 2020, 10, 5806.(SCI)

X. Zhang (联合培养研究生), T. Jing*, W. Zhou, and J. Lei, “Attention-based Contextual Interaction Asymmetric Network for RGB-D Saliency Prediction,” Journal of Visual Communication and Image Representation. https://doi.org/10.1016/j.jvcir.2020.102997(SCI)

2019年

W. Zhou*, L. Yu, Y. Qian, W. Qiu, Y. Zhou, and T. Luo, “Deep blind quality evaluator for multiply distorted images based on monogenic binary coding,” Journal of Visual Communication and Image Representation, vol. 60, pp. 305–311, 2019.(SCI)

W. Zhou*, S. Lv, Q. Jiang, and L. Yu, “Deep Road Scene Understanding,” IEEE Signal Processing Letters, vol. 26, no. 4, pp. 587–591, 2019. (SCI)

W. Zhou*, Y. Zhou, W. Qiu, T. Luo, and Zhinian Zhai, “Perceived quality measurement of stereoscopic 3D images based on sparse representation and binocular combination,” Digital Signal Processing, vol. 93, pp. 128–137, 2019.(SCI)

W. Zhou*, “Blind Stereo Image Quality Evaluation Based on Convolutional Network and Saliency Weighting,” Mathematical Problems in Engineering, 2019.(SCI)

Y. Lv (研究生), W. Zhou*, J. Lei, L. Ye, and T. Luo, “Attention-based fusion network for human eye-fixation prediction in 3D images,” Optics Express, vol. 27, no. 23, pp. 34056–34066, 2019.(SCI)

J. Yuan (研究生), W. Zhou*, and T. Luo, “DMFNet: Deep Multi-Modal Fusion Network for RGB-D Indoor Scene Segmentation,” IEEE Access, vol. 7, pp. 169350–169358, 2019.(SCI)

S. Huang (本科生) and W. Zhou*, “Learning to Measure Stereoscopic S3D Image Perceptual Quality on the Basis of Binocular Rivalry Response,” Applied Sciences, vol. 9, no. 18, pp. 3906. 2019.(SCI)

2018年

W. Zhou*, L. Yu, Y. Zhou, W. Qiu, M.-W. Wu, and T. Luo, “Local and global feature learning for blind quality evaluation of screen content and natural scene images,” IEEE Transactions on Image Processing, vol. 27, no. 5, pp. 2086–2095, May 2018. (SCI)

W. Zhou*, L. Yu, Y. Zhou, W. Qiu, J. Xiang, and Z. Zhai, “Blind screen content image quality measurement based on sparse feature learning,” Signal, Image and Video Processing, vol. 13, no. 3,pp. 525–530, 2019. (SCI)

M. Fang, and W. Zhou*, “Toward an unsupervised blind stereoscopic 3D image quality assessment using joint spatial and frequency representations,” AEU-International Journal of Electronics and Communications, vol. 94, pp. 303–310, 2018.(SCI)

2017年

W. Zhou*, W. Qiu, M. Wu. ”Utilizing dictionary learning and machine learning for blind quality assessment of 3D images,” IEEE Transactions on Broadcasting, vol. 63, no. 2, pp. 404–415, June 2017.(SCI)

W. Zhou*, L. Yu, Y. Zhou, W. Qiu, M.-W. Wu, Ting Luo, “Blind quality estimator for 3D images based on binocular combination and extreme learning machine,” Pattern Recognition, vol. 71, pp. 207–217, Nov. 2017. (SCI)

W. Zhou*, L. Yu, W. Qiu, Y. Zhou, M. Wu, “Local gradient patterns (LGP): an effective local statistical features extraction scheme for no-reference image quality assessment,” Information Sciences, vol. 397-398, pp. 1–14, Aug. 2017.(SCI)

W. Zhou*, S. Zhang, T. Pan, L. Yu, W. Qiu, Y. Zhou, and T. Luo, “Blind 3D image quality assessment based on self-similarity of binocular features,” Neurocomputing, vol.224, pp. 128–134, Feb. 2017. (SCI)

2016年

W. Zhou*, L. Yu, “Binocular responses for no-reference 3D image quality measurement,” IEEE Transactions on Multimedia, vol. 16, no. 6, pp. 1077–1084, 2016. (SCI)

W. Zhou*, L. Yu, W. Qiu, T. Luo, Z. Wang, M. Wu, “Utilizing binocular vision to facilitate completely blind 3D image quality measurement,” Signal Processing, vol. 129, pp. 130–136, Dec. 2016. (SCI)

W. Zhou*, L. Yu, Z. Wang, M. Wu, T. Luo, L. Sun, “Binocular visual characteristics based fragile watermarking scheme for tamper detection in stereoscopic images,” AEU-International Journal of Electronics and Communications, vol.70, no.1, pp. 77–84, 2016. (SCI)

2015年

W. Zhou*, L. Yu, M. Wu. ”Simulating binocular vision for no-reference 3D visual quality measurement,” Optics Express, vol. 23, no. 18, pp. 23710–23715, 2015. (SCI)

W. Zhou*, L. Yu, ”Perceptual quality measurement of 3D images based on binocular vision,” Applied Optics, vol. 54, no. 21, pp. 6549–6557, 2015. (SCI)

W. Zhou*, L. Yu, “No-reference stereoscopic image quality measurement based on generalized local ternary patterns of binocular energy response,” Measurement Science and Technology, vol. 26, no. 9, pp. 095404-1–7, 2015. (SCI)

W. Zhou*, Z. Wang, M. Wu, et al. “Dynamics analysis and circuit implementation of a new three-dimensional chaotic system,” Optik, vol. 126, no. 3–4, pp. 764–768, 2015. (SCI)

2014年

W. Zhou*, G. Jiang, M. Yu, F. Shao, et al. PMFS: A perceptual modulated feature similarity metric for stereoscopic image quality assessment. IEEE Signal Processing Letters, vol. 23, no. 8 , pp. 1003–1006, 2014.(SCI)

W. Zhou, G. Jiang*, M. Yu, F. Shao, et al. Reduced-reference stereoscopic image quality assessment based on view and disparity zero watermarks. Signal Processing: Image Communication, vol. 29, no.1, pp.167–176, 2014.(SCI)

W. Zhou*, G. Jiang*, M. Yu, Z. Wang, F. Shao, et al. Reduced reference stereoscopic image quality assessment using digital watermarking. Computers and Electrical Engineering, vol.40, no.8, pp.104–116, 2014.(SCI)

W. Zhou*, G.Jiang*, M. Yu, F. Shao, et al. Stereoscopic image tamper detection and self-recovery using hierarchical detection and stereoscopic matching. Journal of Electronic Imaging, vol. 23, no.2 , pp. 023022, 2014. (SCI)

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