Curve evolution and estimation-theoretic techniques for image processing

The broad objective of this thesis is the development of statistically robust, computationally efficient, and global image processing algorithms. Such image processing algorithms are not only useful, but in high demand within the image processing arena. Recently, curve evolution and estimation-theoretic approaches to image processing have received considerable attention. Their role in the development of novel image processing algorithms is the focus of this thesis. The main contributions of this thesis lie in the development of three different, but interrelated, image processing algorithms with strong connections to curve evolution and estimation theory. One contribution of this thesis is the development of a new class of computationally-efficient algorithms designed to solve incomplete data problems in which part of the data is not observed, or hidden. These incomplete data problems are frequently encountered in image processing and computer vision. The basis of this framework is the marriage of the expectation-maximization procedure with two powerful methodologies-optimal multiscale estimators and mean field theory. Another contribution of this thesis is the development of a new class of deformable contour models for the segmentation of images which exhibit a known number of features. The key behind this approach is the use of geometric curve evolutions which maximally separate a predetermined set of statistics within the image. In addition, by introducing a geometric constraint on the segmenting curve, we modify this segmentation algorithm to produce a geometric clustering algorithm as well.

Department dc:contributor.department

Harvard University--MIT Division of Health Sciences and Technology.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2001

Author dc:creator

• Tsai, Andy, 1969-

Advisor dc:contributor.advisor

• Alan S. Willsky and Anthony Yezzi, Jr.

• Harvard University--MIT Division of Health Sciences and Technology.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about 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/1721.1/8854

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/8854