报告主题：核方法：从图像分析到深度学习
报告人：陈韵梅 教授 (美国Florida大学)
报告时间：2017年5月31日（周三）10:00
报告地点：校本部G507
邀请人：彭亚新
主办部门：8455新葡萄场网站数学系
报告摘要：In this talk we first present our work on kernel methods for multi-modal image registration and non-parametric image segmentation. The main idea of these methods is using the theory of reproducing kernel Hilbert space (RKHS) to find the nonlinear maps, which can map the images of different modalities to the ones whose intensities are linearly related, by optimizing a finite number of parameters. Inspired by these results, we will further discuss if we could combine the flexibility of kernel methods with the structural and scalable properties of deep neural networks to improve the learning ability of both methods.
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