Date: 2026-11-13

Time: 15:30-16:30 (Montreal time)

Location: In person, Burnside 1104

https://mcgill.zoom.us/j/83129965084

Meeting ID: 831 2996 5084

Abstract:

Images are an increasingly important source of information across scientific disciplines, yet their statistical analysis remains challenging due to the high dimensionality and complex spatial structure of pixel-based representations. In this talk, we explore a functional perspective on image analysis, where images and the objects they contain are represented as continuous functions rather than discrete collections of pixels. We first introduce a framework for representing and aligning object contours, enabling statistical shape analysis and classification. We then extend this approach to multiple interacting contours, preserving their relative geometric relationships. Finally, we discuss the representation of image colors through smooth surfaces, modeled as multidimensional functions. Together, these approaches illustrate how images can be analyzed as collections of objects defined by their shapes, textures, and colors.

Speaker

Cédric Beaulac is a Professor of Statistics in the Department of Mathematics at the Université du Québec à Montréal (UQAM), where he has been a faculty member since 2022. He obtained his Ph.D. in Statistical Sciences from the University of Toronto, under the supervision of Jeffrey S. Rosenthal, before pursuing postdoctoral research at Simon Fraser University and the University of Victoria. His research focuses on statistical learning and functional data analysis, with a particular interest in developing statistical methods for complex, high-dimensional data. His current research program explores functional representations of images through shapes and surfaces, with the goal of developing interpretable and computationally efficient methods for image analysis and statistical learning.