报告人: 台雪成

报告人单位: Hong Kong Baptist University

报告时间:2018年4月2日

报告地点:公司一楼报告厅

报告摘要：

In the talk, we consider an Euler's elastica based image segmentation model. An interesting feature of this model lies in its preference of convex segmentation contour. However, due to the high order and non-differentiable term, it is often nontrivial to minimize the associated functional. In this work, we propose using augmented Lagrangian method to tackle the minimization problem. Especially, we design a novel augmented Lagrangian functional that deals with the mean curvature term differently as those ones in the previous works. The new treatment reduces the number of Lagrange multipliers employed, and more importantly, it helps represent the curvature more effectively and faithfully. Numerical experiments validate the efficiency of the proposed augmented Lagrangian method and also demonstrate new features of this particular segmentation model, such as shape driven and data driven properties.

 报告人简介：

台雪成,香港浸会大学教授，第8届“冯康”计算数学奖获得者. 台雪成教授的研究领域主要包括数值PDE、优化技术、计算机视觉以及图像处理等，在SIAM J. Sci. Comput.、International Journal of Computer Vision、IEEE Transactions on Image Processing、IEEE Transactions on Visualization and Computer Graphics、SIAM J. Numer. Anal.等国际顶级杂志以及CVPR、ECCV等国际顶级会议共发表论文100多篇. 担任多个国际会议的大会主席，并多次应邀做大会报告，目前担任Inverse Problems and Imaging，International Journal of Numerical analysis and modelling，Numerical Mathematics: Theory, Methods and Applications，Advances in Numerical Analysis, SIAM Journal on Imaging Sciences, Journal of Mathematical Imaging and Vision等多个国际知名期刊的编辑及执行编辑.