学术报告
Geometrical and Variational Priors for CNN Image Segmentation - 刘君 副教授（北京师范大学）
题目：Geometrical and Variational Priors for CNN Image Segmentation
报告人：刘 君 副教授（北京师范大学）
Abstract：Convolutional Neural Networks (CNN) can well extract the features from natural images. However, the classification functions in the existing network architecture of CNNs are always simple and lack capabilities to handle important spatial information in a way that have been done for many well-known traditional variational models. Priors such as spatial regularization, volume, object shapes, topology priors cannot be well handled by existing CNN architectures. We propose a novel Soft Threshold Dynamics (STD) based framework which can easily integrate many priors such as local and nonlocal image edges information, star/convexity shapes, topology priors (connectivity and holes) of the classic variational models into the DCNNs for image segmentation. The novelty of our method is to interpret the activation functions (including softmax, sigmoid, ReLU) as primal-dual variational problem, and thus many priors can be imposed in the dual space. By unrolling method, we can build several STD based network architectures which can enable the outputs of CNN to have many special priors. The proposed method is a general framework and it can be applied to any image segmentation CNNs. We will give some applications to show the efficiency of our method.
报告人简介：刘 君，北京师范大学副教授，博士生导师。曾受邀访问过美国UCLA、新加坡南洋理工、香港科技大学、香港浸会大学等高校。主要研究方向为变分法及深度学习相关的图像处理算法与应用。一些研究结果发表在图像处理与计算机视觉相关领域国际知名期刊如Int. J. Comput. Vis., IEEE T. Image. Process., IEEE T. Geosci. Remote, Pattern Recogn., SIAM J. Imaging Sci., J. Sci. Comput., J. Math. Imaging Vis. 等。研究成果曾获教育部高等学校优秀科研成果二等奖（团体）, 北京市科技进步二等奖（团体）。主持参与多项国家科研项目。
时间：2020年11月12日（周四）下午18:30-20:30
线上腾讯会议号：555 665 424
联系人：李宏伟
主办单位：首都师范大学77779193永利官网
检测成像北京市高校工程研究中心
