Generative Models for Computer Vision

In order to build robust computer vision algorithms, scene models are necessary that are capable of capturing various aspects of the data at the same time. These models should be fairly simple, but capable of adapting to the data. Flexible models, as defined in the machine learning community, are minimally structured probability models with a large number of parameters that can adapt so as to explain the input data. We describe one possible framework for designing and using flexible models for vision. The framework uses structured probability models to describe causes of variability in the data, exact or variational methods for inference, and an expectation-maximization algorithm for parameter estimation. We show that within this framework, we can perform various vision tasks jointly, such as tracking, recognition, occlusion detection, object stabilization, object removal, and filtering. In fact, in this dissertation we argue that dealing with these tasks jointly is easier than combining individually optimized modules in a typical engineering approach to signal processing.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Jojic, Nebojsa

Contributors dc:contributor

• Huang, Thomas S.
• Brendan J. Frey

• Engineering, Electronics and Electrical

Language dc:language

eng

Identifier

(MiAaPQ)AAI3070010

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/80778