Simek, Kyle LouisView Profile. (The University of Arizona), “Branching Gaussian process models for computer vision” (2016) Advisor: Jacobus Kobus Barnard
Bayesian methods provide a principled approach to some of the hardest problems in computer vision—low signal-to-noise ratios, ill-posed problems, and problems with missing data. This dissertation applies Bayesian modeling to infer multidimensional continuous manifolds (e.g., curves, surfaces) from image data using Gaussian process priors.