[CMU Thesis Proposal] Recognizing Object Structures - A Bayesian Framework for Deformable

Title: Recognizing Object Structures - A Bayesian Framework for Deformable Matching

Author : Leon Gu

Abstract:

When we look at images of human faces, cars and peoples, we do not perceive them as collections of pixels, we perceive the structures. Image understanding requires uncovering details of object structures. One of the most promising ways for this task is through deformable template matching. However, traditional deformable models have been largely limited to images with sharp contrast, clean background or restricted testing samples. One of the reasons for this is the lack of a principled statistical framework for deformable matching under general imaging conditions.

In this thesis, we propose a Bayesian framework which describes structure deformation, geometrical transformation and image evidence in a three-layered generative model. Deformable matching is viewed as a Bayesian inference procedure. In particular, we will show how to solve a few typical matching problems in this framework, for instance, how to control shape smoothness when images are noisy, how to identify outliers on background clutters or occluded regions, how to make use of multi-modal shape priors, and how to infer missing geometrical informations such as depth from single image. One appealing point of the proposed work is that all these problems are solved in a consistent way. We demonstrate the applications of this theory in recovering 2D/3D facial structures and human body configurations from still images.