報告人：Prof. Jean Sequeria 教授
法國，1953年生，1982年獲得法國頂尖工程師大學巴黎綜合理工大學博士學位。Sequerira博士1981-1991年在IBM巴黎科學中心任高級工程師，1991年起任法國艾克斯-馬塞大學的終身教授，是法國信息與科學管理實驗室學科負責人,法國LSIS實驗室學科帶頭人，并于2010年成為法國Exceptional級别教授（top 1%）。目前，他是國際數字地球學會執行委員會成員、IEEE高級會員、中國科學院遙感所特邀教授。Sequeira博士的研究領域主要包括圖像分析、模式識别、幾何建模和可視化等。
報告時間：2018年6月3日9：00-10：00am
報告地點：伟德BETVLCTOR202
報告内容：
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.