Graphical models for segmenting images, volumes and videos

Thesis (Ph. D.)--University of Rochester. Dept. of Computer Science, 2013.Image segmentation is a fundamental problem in computer vision. It can be defined as the process of labeling each pixel in the image such that pixels with the same label share some common pre-defined attribute. Thus, the image is broken into groups of pixels that are easier to analyze for the task at hand. In this thesis, we focus on the problem of object segmentation where the pre-defined attributes are objects. Markov random fields (MRF) are undirected graphical models with a probability distribution attached to it. The nodes in the graph are random variables and the interaction between these random variables follow the Markov property. MRFs have generative properties. The discriminative version of MRFs called conditional random fields (CRF) have a conditional distribution attached to the graphs. Both MRFs and CRFs are useful tools for modeling image segmentation problems. In this thesis, we explore the use of conditional random fields and hybrid graphical models consisting of generative and discriminative parts for image segmentation. In the first part of the thesis, we consider the problem of multi-class segmentation of the abdominal organs in medical images. The graphical models are 3D graphs and we use different features to improve segmentation on these medical images. In the second part of the thesis, we segment people from videos. The method makes no assumption about camera motion or location, scale or motion patterns of the person. In the third part of the thesis, we focus on the problem of inference in graphs. Inference is the process of prediction of labels of the graph and is also needed to learn the parameters of the model. We discuss a new method to do inference in graphs that improves the existing dual decomposition method