报告人：Prof. Jean Sequeria 教授
法国，1953年生，1982年获得法国顶尖工程师大学巴黎综合理工大学博士学位。Sequerira博士1981-1991年在IBM巴黎科学中心任高级工程师，1991年起任法国艾克斯-马塞大学的终身教授，是法国信息与科学管理实验室学科负责人,法国LSIS实验室学科带头人，并于2010年成为法国Exceptional级别教授（top 1%）。目前，他是国际数字地球学会执行委员会成员、IEEE高级会员、中国科学院遥感所特邀教授。Sequeira博士的研究领域主要包括图像分析、模式识别、几何建模和可视化等。
报告时间：2018年6月3日9：00-10：00am
报告地点：beat365官方最新版202
报告内容：
The Hough Transform: a model-driven approach for detecting items of a specific model within a Data Set
Hough Transform is a classical “model driven” approach for Image Analysis. The first works made by Paul Hough are from about 60 years old, but there is still an active research and publications on it. In this talk, I will give a very brief history of the Hough Transform, then give more details on the detection of specific models (as lines, circles and ellipses) within a formal scheme, and then I will explain it can be used in a more general frame (and not only to detect curves in images), as for example, the characterization of a geometric transformation. All of that will be illustrated by examples.
