Graduate Statistics Seminar
Nick Syring
UIC
Gibbs Models for Identification of Image Boundaries
Abstract: I will introduce the problem of identifying boundaries in images observed with random noise. I will present a Gibbs model solution, which combines elements of machine learning and Bayesian statistics. I have produced a proof that the proposed model converges at the minimax rate, and I show through simulations the competitive performance of the Gibbs model. If there is sufficient interest, I may share the details of the proof at a later date.
Tuesday February 16, 2016 at 4:00 PM in SEO 636