作者:聚创电子科大考研网-小厦老师
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发布时间: 2015-07-27 18:00
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个人简介
文泉,副教授
联系方式
邮箱:qwuta@163.com
教育背景
1990-1994:四川大学原子核科学与技术研究所(720)自动控制本科
1994-1997:四川大学计算中心计算机应用硕士 (Sichuan University, MS in Computer Application )
2003-2008:美国德克萨斯大学阿灵顿分校计算机科学与工程博士 (University of Texas at Arlington, Ph.D. in Computer Science and Engineering)
2008-2009:美国伯克利实验室生命科学部博士后 (Berkeley Lab Life Sciences Division, Postdoc)
科研方向
图像处理,模式识别,机器学习,大数据处理
研究项目
2011.01-2013.12:国家自然科学基金面上项目 (National Natural Science Foundation Supported),项目负责人
2010.05-2013.05:电子科技大学计算机科学与工程学院杰出人才培育计划资助,项目负责人
电子科技大学科研启动基金,项目负责人
2009.08-2012.08:电子科技大学计算机科学与工程学院科研启动基金,项目负责人
2009-现在:电子科技大学计算机科学与工程学院副教授。主要研究领域为医学和生物医学图像信息,包括:基于皮肤镜的黑色素瘤自动诊断研究,基于虚拟切片的实体肿瘤自动诊断研究,基于切片的病理组织自动分析研究,基于显微镜图像的生物医学研究等,涉及病理切片、CT、MRI、PET、LSCM等图像和视频处理,以及相关的数据挖掘,模式识别,机器学习理论。
2008-2009:美国伯克利国家实验室生命科学部博士后(Berkeley National Laboratory Life Science Division, Postdoc)。研究基于图像技术的基因型/表现型(Genotype/Phenotype)关系, 生物医学图像处理, 生物信息处理, 图像处理, 模式识别, 机器学习参与科研项目:
(1)由核酸识体揭示的基因表达(美国能源部支持)(GRABIT: Gene Expression Revealed by Aptamer, Supported by Department of Energy)
(2)癌症基因组图谱(美国国立卫生研究院支持)(The Cancer Genome Atlas,Supported by National Institutes of Health)
2003-2008:德克萨斯大学阿灵顿分校,生物计算与视觉实验室 , 博士(University of Texas at Arlington, Biocomputing and Vision Lab, Ph.D.). 研究生物医学图像信息,图像处理, 模式识别。参与科研项目:
(1)用于细胞运动研究的4维亚细胞结构的跟踪与建模(美国国家科学基金) (Four-Dimensional Subcellular Structure Tracking and Modeling for Cell Dynamics Study ,supported by National Science Foundation)
(2)用于整容手术的计算机人体血管三维构造的研究 (3D blood vessel reconstruction for plastic surgery)
(3)视频检索和分析,包括镜头检测(Shot Detection)、视频摘要(Video Abstraction)、异常行为检测(Abnormal Detection)等
论文列表
[1] M. E. Celebi, S. Hwang, Q. Wen, “Colour quantisation using the adaptive distributing units algorithm”, The Imaging Science Journal, vol. 62, no. 2, pp. 80-91, Feb. 2014. (SCI-E)
[2] M. E. Celebi, Q. Wen, S. Hwang, H. Iyatomi, and G. Schaefer, “Lesion Border Detection in Dermoscopy Images Using Ensembles of Thresholding Methods,” Skin Research and Technology, vol. 19, no. 1, pp. e252�Ce258, 2013. (SCI-E)
[3] Q. Wen and M. E. Celebi, “Hard Versus Fuzzy C-Means Clustering for Color Quantization,” EURASIP Journal on Advances in Signal Processing, vol. 2011, no. 1, p. 118, Nov. 25, 2011. (SCI-E)
[4] Q. Wen, D. Ming, J. Chen, and W. Liu, “A superpixel based post-processing approach for segmenting dermoscopy images,” in 2013 IEEE International Conference on Advanced Computational Intelligence, Oct. 19-21 2013, pp. 155�C158.
[5] D. Ming, Q. Wen, J. Chen, and W. Liu, “A generalized fusion approach for segmenting dermoscopy images using Markov random field,” in 2013 IEEE International Congress on Image and Signal Processing, Dec. 16-18 2013, pp. 533�C537.
[6] Q. Wen, D. Ming, J. Chen, and W. Liu, “A novel fusion approach for segmenting dermoscopy images based on region consistency,” in 2013 IEEE International Conference on Computational Problem-Solving, Oct. 27-28 2013.
[7] Q. Wen, J. Chen, and W. Liu, “Biologically inspired classification of microvessel histopathology via sparse coding,” in 2013 IEEE International Conference on Advanced Computational Intelligence, Oct. 19-21 2013, pp. 114�C118.
[8] Q. Wen, J. Chen, and W. Liu, “A predictive coding approach on microvessel identification via single-opponent signal,” in 2013 IEEE International Congress on Image and Signal Processing, Dec. 16-18 2013, pp. 1530�C1534.
[9] M. E. Celebi and Q. Wen, “Variance-cut: A fast color quantization method based on hierarchical clustering,” in 2013 IEEE International Conference on Electronics, Computer and Computation, Nov. 7-9 2013, pp. 103�C106.
[10] Q. Wen, J. Chen, and W. Liu, “Quantitative analysis on mobility behaviors of fluorescent marker proteins using graph model,” in 2013 IEEE International Congress on Image and Signal Processing, Dec. 16-18 2013, pp. 670�C674.
[11] Q. Wen, W. Qu, J. Chen, and M. Mete, “A novel method for counting subcellular structures labeled by green fluorescent protein,” in 2012 3rd IEEE International Conference on Computational Problem-Solving, Oct. 19-21 2012, pp. 500�C503.
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