报告时间：2021年11月16日16:00-17:30（北京时间）
报告方式：
线上：腾讯会议（ID:892 288 474）
线下：1066vip威尼斯宝山校区东区9号楼802室
报告信息
报告题目：高光谱成像简介：无损检测和基于质量分级的工业自动化中的新兴技术和新应用
Introduction to Hyperspectral Imaging: Emerging Techniques and New Applications in Non-destructive Inspection and Quality Grading Based Industrial Automation
报告摘要：As an emerging technique, hyperspectral imaging (HSI) has been successfully applied in a number of applications, thanks for its unique characteristics in sensing and measuring several physical and chemical properties of the targets, e.g. moisture, temperature and chemical components. In this talk, the concepts of HSI will first be introduced, followed by discussions of the associated challenges and some solutions. Several case studies will be highlighted, especially for its applications in non-destructive inspection and quality grading based industrial automation, in the context of an EU H2020 project.
报告人简介：Prof. Jinchang Ren is with the National Subsea Centre and School of Computing, Robert Gordon University, Aberdeen, U.K. He received BEng in Computer Software, MEng in Image Processing and Pattern Recognition, and DEng in Computer Vision, all from the Northwestern Polytechnical University, Xi’an, China. He was also awarded a PhD in Digital Media and Communications from the University of Bradford, Bradford, U.K. He is a Senior Member of IEEE.
His research interests focus mainly on image processing, computer vision, machine learning and big data analytics, especially the hyperspectral imaging, remote sensing and multimodal image fusion. With a research portfolio over £3.5 million, his research is funded by UKRI, EU, ONR, and a number of industrial partners. He has published over 300 articles in leading journals and conferences, including about 150 cited by SCI. He has been sitting in the editorial board of a few SCI journals, including IEEE Trans. Geoscience and Remote Sensing, Journal of the Franklin Institute, IEEE JSTARS et al. His students have been awarded a few Best Paper and Best Poster prizes in different conferences and workshops, esp. the Best PhD thesis from IET Image and Vision Section in 2016.
