报告题目：Solving Graph-Cut-Based Optimization Problems by a Smoothing Technique with Applications in Multiphase Segmentation
      报告人：殷钶（副研究员，华中科技大学数学中心）

照片：

邀请人：袁景  教授
      报告时间：2018年12月10号（星期一）下午4点
      报告地点：信远楼II206数统院报告厅

报告人简介：Prof. Ke Yin is the associate professor in Center for Mathematical Sciences at Huazhong University and Science and Technology. He has been an Adjunct Assistant Professor and postdoctoral fellow in Department of Mathematics at UCLA from 2013 to 2016. Before that, he obtained B.Sc. from University of Science and Technology of China, and Ph.D. from Georgia Institute of Technology in USA. Prof. Yin’s research interests include inverse problems, non-smooth optimization with applications in machine learning and image processing.

报告摘要：Multi-labeling problems, such as multi-phase segmentation on images, can be proposed as a graph-cut based optimization problem through modification of Pott’s model. Solving the original Pott’s model is known as a combinatorial optimization, which poses difficulty in reducing computational complexity. The continuous max-flow approach proposed by Yuan, Tai, et al has been demonstrated to be efficient in semi-supervised multi-phase segmentation. It is formulated as a convex, yet non-smooth optimization problem. Several algorithms have been proposed, such as solving the dual problem by proximal gradient, primal-dual hybrid gradient, augmented Lagrangian method, and more recent Bregman-proximal augmented Lagrangian method. In this talk, we are going to review these algorithms and propose a new one, based on a smoothing technique for the dual problem, which has several advantages such as theoretical guarantee of convergence with proved speed and error estimate for early termination. Some numerical examples in multi-phase segmentation are shown to demonstrate its effectiveness.

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