Analyzing and synthesizing deformations in image datasets

Many tasks in computer vision and graphics, such as image registration, optical flow estimation and image warping, are concerned with measuring spatial deformations between images. Traditional algorithms for these applications often rely on solving an optimization problem for each test input. Some of these methods produce poor results on complex problems. For example, image warping methods struggle with non-planar objects and occlusions. Furthermore, for large inputs, some of these algorithms can be quite slow. For instance, a state-of-the-art medical image registration algorithm takes over 2 hours on a CPU to register a pair of 3D volumes. In recent years, learning methods have proven successful in a variety of computer vision applications. This thesis first presents neural network models to address two image deformation tasks: registration and warping.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Balakrishnan, Guha.

Advisor dc:contributor.advisor

• John V. Guttag.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/118029

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/118029