4月25日下午2:30，应best365亚洲版登录袁景教授邀请，加拿大魁北克大学Ismail Ben Ayed 教授于在信远楼II区206报告厅做了题为“High-Order Graphs in Computer Vision: A Pseudo-Bound Optimization Approach”的学术报告。此次学术报告由袁景老师主持，我院部分教师和研究生参加了此次学术报告会。

报告会之前，袁老师首先为大家简单介绍了Ismail 教授。接着Ismail 教授介绍了近年来出现在计算机视觉和机器学习领域中的一些难以优化功能的问题，例如聚类、语义分割、图像去卷积以及曲率正则化等。同时，他针对这些问题讲述了最近的一些发展，重点讲解了伪约束优化框架。然后，Ismail 教授通过具体例子为我们讲述了该框架的关键技术，并展示了如何改进视觉学习问题中相关技术的方法，包括约束图聚类以及各种语义图像分割示例。

此次报告会不仅让我们了解到计算机视觉中的伪绑定优化方法等相关学术知识，同时也拓宽了大家的视野，了解到国际上针对于此问题的最新进展，让大家认识注重理论与实际相结合。

报告人简介：

Ismail Ben Ayed received the PhD degree (with the highest honor) in computer vision from the INRS, University of Quebec, Montreal, QC, in 2007. He is currently Associate Professor at the ETS, University of Quebec, where he holds an institutional research chair on artificial intelligence in medical imaging. Before joining the ETS, he worked for 8 years as a scientist at GE Healthcare, London, ON, where he conducted research in medical image analysis. He also holds an adjunct professor appointment at Western University (since 2012). Ismail’s interests are in optimization, computer vision, machine learning and their potential applications in medical image analysis. He co-authored a book and over seventy peer-reviewed publications, mostly published in the top venues in these subject areas. During his experience with GE, he received the GE innovation award (2010) and filed seven US patents. Dr. Ben Ayed serves regularly as program committee member for the flagship conferences of the field, and as regular reviewer for the top journals. He received the outstanding reviewer award for CVPR in 2015.