讲座人简介： 刘君，北京师范大学副教授，博士生导师。2011年博士毕业于北师大，2012年香港科技大学博士后。新加坡南洋理工大学、香港浸会大学、美国UCLA访问学者。主要研究方向为变分法、机器学习、最优传输相关的图像处理算法与应用。研究成果发表在图像处理、计算机视觉、计算数学等领域权威期刊及会议如IJCV、CVPR、IEEE TIP、PR、SIIMS、IP、JSC等。主持多项国家及省部级科研基金。作为核心团队成员，参与多项国家重大、重点科研项目，相关研究成果随团队荣获自然资源部科技进步一等奖、教育部高等学校优秀科研成果二等奖、北京市科技进步二等奖。

讲座简介： Multi-center star shape is a prevalent object shape feature, which has proven effective in model-based image segmentation methods. However, the shape field function induced by the multi-center star shape is non-smooth, and directly applying it to the data-driven image segmentation network architecture design may lead to instability in backpropagation. This paper proposes a convex combination star (CCS) shape, possessing multi-center star shape properties, and has the advantage of effectively controlling the shape of the region through a smooth field function. The sufficient condition of the proposed CCS shape can be combined into the image segmentation neural network structure design through the bridge between the variational segmentation model and the activation function of the data-driven method. Taking Segment Anything Model (SAM) and its improved version as backbone networks, we have shown that the segmentation network architecture with CCS shape properties can greatly improve the accuracy of segmentation results.