Getting Your Computer Into Shape: Modeling Objects in Two and Three Dimensions
Kathryn Leonard’s research interests are in geometric modeling with applications to computer vision, computer graphics, and data science. Her work has been recognized with a CAREER award from NSF, the Henry L. Alder Award for Excellence in Teaching from the MAA, and a Service Award from the Association of Women in Mathematics (AWM). She became a math major in her junior year of college, after her petition to waive the university’s math GE requirement was rejected. Currently, she is President of AWM. She also directs the NSF-funded Center for Undergraduate Research in Mathematics, leads the AWM research networks for Women in Shape and Women in the Data Science and Mathematics, and is on the Board of Directors of Steam:Coders, a local non-profit working to make computing education accessible to all. She has held positions at CSU Channel Islands, where she helped build a university, Caltech, and MSRI, and is currently at Occidental College. She still gets no respect from her cat.
Shape understanding—looking at a shape and intuitively understanding which parts are, e.g., body, arms, legs, toes, and ears—is almost effortless for humans. Training a computer to understand shapes in a similar way, however, presents substantial challenges. This talk will discuss human shape perception and the challenges of automation. We will describe a useful mathematical shape model, the Blum medial axis (BMA), and a method based on the BMA for automatically decomposing a shape into a hierarchy of parts and determining the similarity between those parts. We compare our automated results to human perception data gathered from a massive user study, and also provide some useful applications. Time permitting, we will present recent work and an open annual challenge to use neural networks to learn the BMA from input images. We’ll also share personal stories of how we ended up doing this work.
Friday, February 11, 2022, at 4 p.m. (PT) in HMC‘s Shanahan Center 1430