Unified approach for the early understanding of images

In the quest for computer vision, that is the automatic understanding of images, a powerful strategy has been to model the image parametrically. Two prominent kinds of approaches have been those based. on polynomial models and those based on random-field models. This thesis combines these two methodologies, deciding on the proper model by means of a general decision criterion. The unified approach also admits composite polynomial/random-field. models and is applicable to other statistical models as well. This new approach has advantages in many applications, such as image identification and image segmentation. In segmentation, we achieve speed by avoiding iterative pixel-by-pixel calculations. With the general decision criterion as a sophisticated tool, we can deal with images according to a variety of model hypotheses. Our experiments with synthesized images and real images, such as Brodatz textures, illustrate some identification and segmentation uses of the unified approach.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Electrical Engineering

Department dc:contributor.department

Electrical Engineering

Grantor dc:publisher

Virginia Polytechnic Institute and State University

Year dc:date.issued

1985

Author dc:creator

• Jeong, Dong-Seok

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Handle dc:identifier.uri

http://hdl.handle.net/10919/50028

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/50028