时间：2022 年10 月6 日（周四）15:00-16:00

地点：腾讯会议ID：139-980-467

题目：Curvature Regularization: Models, Algorithms and Applications

摘要： The geometric high-order regularization methods such as Euler’selatica, mean curvature, and Gaussian curvature, have been intensively studied during the last decades due to their abilities in preserving geometric properties including image edges, corners, and image contrast. However, the dilemma between restoration quality and computational efficiency is an essential roadblock for high-order methods. We propose novel curvature regularization models and develop fast multi-grid algorithms without sacrificing the accuracy for efficiency. Besides, we also extend the application

of curvature energies for soft tissue simulation. An efficient nonlinear mass-spring model is developed by introducing the elastica springs, which measure the soft tissue deformation based on both spring length and curvature.

We implement the proposed model as the physical engine for a prototype of anatomical virtual reality, where realistic deformation is rendered at a refresh rate of 33frames/s on a regular personal computer.

报告人简介： 段玉萍，天津大学应用数学中心, 教授，博士生导师，国家青年千人计划入选者。2007 至2011 年在新加坡南洋理工大学攻读博士学位。2012 年至2015 年在新加坡科技研究局资讯通信研究院工作，参与过多个医学工程项目。主要从事数字图像处理等领域的研究。在IEEE Trans.