Topic: Variational Framework for Image Vectorization and Applications

Lecturer: Prof. He Yuchen, City University of Hong Kong

Time: March 20, 2026, 15:00, UTC+8

Venue: Room 213, Mathematics and Statistics Building

Biography: Dr. He Yuchen obtained her Ph.D. in Mathematics from the Georgia Institute of Technology in 2021. Between 2020 and 2023, she served as a researcher at the École Normale Supérieure Paris-Saclay in France, Duke University, and Shanghai Jiao Tong University. She joined the Department of Mathematics at City University of Hong Kong as an Assistant Professor in 2023. Her main research areas include variational methods in image processing, computational geometry, data-driven inverse problems, deep learning theory, and their applications in computer vision. Related research results have been published in internationally authoritative academic journals such as SIAM Journal on Imaging Sciences, Journal of Computational Physics, and Nature Communications.

Abstract: Images are commonly represented as bitmaps, and it is crucial to identify intrinsic geometric features of objects in such an unstructured format. Vectorization is a popular technique that converts raster images into a collection of parametric curves and surfaces, encoding the input's prominent features and yielding resolution-independent representations. In this talk, we propose variational principles for image vectorization along with effective algorithms based on the affine shortening flow and region merging, generalizing a steepest gradient descent for the reduced Mumford-Shah functional. We will also present recent applications in shape classification and historical glyph preservation.

Rewritten by: Li Huihui

Edited by: Li Tiantian

Source: School of Mathematics and Statistics