黄可坤 Huang Kekun

教授/Professor

嘉应学院

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Biography

黄可坤,广东省梅州市人,分别于2002年、2005年和2016年在中山大学应用数学专业获得本科、硕士和博士学位,从事图像处理与模式识别方面的研究。2005年到嘉应学院任教,2013年晋升为副教授,2019年1月晋升为教授。在IEEE TNNLS, TGRS, TIP, TCYB和Pattern Recognition等二区以上的国际著名期刊发表了论文10多篇,主持2项国家自然科学基金项目《基于稀疏表示和多核学习的人脸识别方法研究》和《高光谱图像的深层判别特征提取方法》2018年1月起聘为 嘉应学院高水平应用型大学建设高层次人才。2018年被评为广东省南粤优秀教师。

He is currently a Professor with the Department of Mathematics, Jiaying University. His current research interests include pattern recognition and hyperspectral image analysis based on deep learning. He has authored or co-authored over 10 refereed technical papers, including IEEE TNNLS, IEEE TGRS, IEEE TIP, IEEE TCYB and Pattern Recognition. He was elected as NanYue Excellent Teacher of GuangDong province. He is a member of IEEE. Teaching homepage: http://kkcocoon.gotoip2.com/ .

Education

2013/08 – 2016.06, Ph.D. in Applied Mathematics, Sun Yat-sen University. Advisor: Prof. Dao-Qing Dai.

2002/09 – 2004/12, M.Sc in Applied Mathematics, Sun Yat-sen University. Advisor: Prof. Dao-Qing Dai.

1998/09 – 2002/06, B.S. in Applied Mathematics, Sun Yat-sen University.

Work Experience

2019/01 – now, JiaYing University, Department of Mathematics, Professor.

2013/12 – 2018/12, JiaYing University, Department of Mathematics, Associate Professor.

2007/12 – 2013/12, JiaYing University, Department of Mathematics, Assistant Professor.

2005/02 – 2007/12, JiaYing University, Department of Mathematics, Teaching Assistant.

Honor

2018年评为广东省南粤优秀教师

2018年1月起聘为 嘉应学院高水平应用型大学建设高层次人才

2014年评为嘉应学院首届方直卓越教师

2007-2013连续7年获得课堂教学质量优秀奖,多次年度考核优秀

指导数学建模竞赛获多项国家奖和省级奖,多次获评广东省数学建模竞赛优秀指导教师

2008年获得嘉应学院青年教师教学竞赛一等奖

Academic Activities

IEEE member

广东省工业与应用数学学会理事

Reviewer of IEEE TNNLS, TCYB, TCSVT, TGRS, CVPR,Pattern Recognition, Digital Signal Processing, Neurocomputing, Current Medical Imaging Reviews, etc.

Teaching

Teaching homepage: http://kkcocoon.gotoip2.com/

Project

[1] 高光谱图像的深层判别特征提取方法 .国家自然科学基金面上项目(61976104) 202001月至202312月,直接经费60. 黄可坤主持.

[2] 基于稀疏表示与多核学习的人脸识别方法研究 .国家自然科学基金青年项目(61403164) 201501月至201712月,经费23. 黄可坤主持.

[3] 基于稀疏表示的遥感图像分类的特征提取方法研究 .广东省教育厅高校优秀青年创新人才培养计划项目(2013LYM_0085),时间2014.1-2015.12, 经费3 . 黄可坤主持.

[4] 濒危语言有声语档建设软件开发 .国家社科重点项目的横向项目(12AYY02). 黄可坤.

[5] 微信公众号:KK人脸识别拍照点名。"KK人脸识别拍照点名" 是一个基于微信公众号的应用。教师上课时,对学生拍若干张照片, 使得每个同学都有无遮挡的比较大的正面照。然后向此微信公众号发送照片, 就可以返回出勤的人的列表和缺勤的人的列表。

[1] Deep discriminative feature extraction method for hyperspectral image classification. National Science Foundation of China (61976104), 2020.01-2023.12.

[2] Face recognition based on sparse representation and multiple kernel learning. National Science Foundation of China (61403164), 2015.01-2017.12.

[3] Hyperspectral image classification based on sparse representation. the Foundation for Distinguished Young Talents in Higher Education of Guangdong, China (2013LYM_0085). 2014.1-2015.12.

[4] Software development for the construction of endangered language vocabulary files. Horizontal project of key national social science projects (12AYY02). 2012.8-2014.12

[5] Face Photograph Check based on WeChat Public Number (kkFaceCheck).

Representative paper

[1] Ke-Kun Huang, Dao-Qing Dai, Chuan-Xian Ren, Zhao-Rong Lai. Learning Kernel Extended Dictionary for Face Recognition. IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 5, pp. 1082-1094, 2017. (matlab source code)(PDF)(SCI一区)

[2] Ke-Kun Huang, Dao-Qing Dai, Chuan-Xian Ren,Yu-Feng Yu, Zhao-Rong Lai. Fusing Landmark-based Features at Kernel Level for Face Recognition. Pattern Recognition, vol 63, pp. 406-415, 2017. (PDF)(SCI二区)

[3] Ke-Kun Huang, Dao-Qing Dai and Chuan-Xian Ren. Regularized Coplanar Discriminant Analysis for Dimensionality Reduction. Pattern Recognition, vol. 62, no.2, pp. 87-98, 2017.(matlab source code)(PDF)(SCI二区)

[4] Ke-Kun Huang, Dao-Qing Dai. A new on-board image codec based on binary tree with adaptive scanning order in scan-based mode. IEEE Transaction Geoscience and Remote Sensing, vol. 50, no. 10, pp. 3737-3750, 2012.(matlab source code) (PDF) (SCI二区)

[5] Ke-Kun Huang, Hui Liu, Chuan-Xian Ren, Yu-Feng Yu and Zhao-Rong Lai. Remote sensing image compression based on binary tree and optimized truncation. Digital Signal Processing, vol. 64, pp. 96-106, 2017. (matlab source code) (PDF) (SCI三区)

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