报告题目： Hierarchical Neural Networks with Subnetwork Neurons for Image Understanding
报 告 人： 武庆明（Q. M. Jonathan Wu） 教授
时 间： 2017年9月25日（周一）下午3：30
地 点： js33333金沙线路检测 青岛校区 振声苑南楼S204
摘 要：
Most of actual images such as human face images, industrial images and MRI images are high-dimensional data. The feature representation is mainly for the purpose of extracting useful information and of using this information to build non-supervised classifier/supervised classifier or other types of predictor because the image processing performance is often closely related to the feature data extracted and used. In this talk, we propose a generalized ELM-Deep learning framework which is intended to extract the optimized features. Then, we extend and apply this method for such application fields as dimension reduction, image identification, and image reconstruction, etc. Compared with other feature representation methods, the experimental results show that the generalization performance of the proposed generalized learning framework is very advantageous. A brief overview of other related research activities in the presenter’s laboratory related to image analysis, computer vision, and machine learning is also provided. Applications have been extended towards intelligent transportation systems, surveillance and security, face and gesture recognition, vision-guided robotics, and bio-medical imaging, among others.
报告人简介：
武庆明（Q. M. Jonathan Wu） 教授，现任加拿大温莎大学电子工程系教授，博士生导师，计算机视觉和传感系统研究所主任，长期从事图像处理，模式识别与智能系统的教学与研究工作，先后主持完成加拿大国家科学与工程研究项目(NSERC)，国际合作重大项目、加拿大国家重点基金项目，加拿大汽车电子和信息系 统领域的Canada Research Chair。至今共培养博士、博士后35人。现任国际杂志《IEEE Transaction on Neural Networks and Learning Systems》、《International Journal of Robotics and Automation》与《Cognitive Computation》副主编，《IEEE Computational Intelligence Magazine》客座编委。在过去的研究工作中，申请人对图像实时分割、图像压缩与特征提取、图像去噪与识别、三维重建，机器学习等问题进行了深入研究，SCI 收录论文约150篇，IEEE Transactions 论文50 余篇，在图像处理、智能信息处理、机器学习与模式识别领域，取得多项重大研究成果。