Nick Syring : Gibbs Models for Identification of Image Boundaries

Posted by Nicholas Syring , part of the Graduate Statistics Seminar.

At

Feb. 16, 2016, 4 p.m.

In

SEO 636

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.