Deep neural networks in image processing

讲座时间 Datetime: 2022年9月23日，星期五，10:00-11:30

地点 Venue: zoom会议 753 4229 6514  密码：v5Zux1

主持人 Host：时聪

报告人 Speaker: 台雪成 

单位 Affiliation: 香港浸会大学

报告摘要 Abstract:

_{In this talk, we present our recent research on using variational models as layers for deep neural networks (DNNs). We use image segmentation as an example. The technique can also be used for high dimensional data classification as well. Through this technique, we could integrate many well-know variational models for image segmentation into deep neural networks. The new networks will have the advantages of traditional DNNs. At the same time, the outputs from the new networks can also have many good properties of variational models for image segmentation. We will present some techniques to incorporate shape priors into the networks through the variational layers. We will show how to design networks with spatial regularization and volume preservation. We can also design networks with guarantee that the output shapes from the network for image segmentation must be convex shapes/star-shapes. It is numerically verified that these techniques can improve the performance when the true shapes satisfy these priors.}

_{The ideas of these new networks is based on some relationship between the softmax function, the Potts models and the structure of traditional DNNs. We will explain this in detail which leads naturally to the newly designed networks.}

_{This talk is based on joint works with Jun Liu, S. Luo and several other collaborators.}