{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/50028"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/50028","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Unified approach for the early understanding of images","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Jeong, Dong-Seok"],"institution":"Virginia Polytechnic Institute and State University","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Electrical Engineering","degree_department":"Electrical Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":1985,"date_issued":"1985","date_published":"1985","updated_at":"2026-07-22T22:20:03Z","subjects":[],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10919/50028","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.department","label":"Department","values":["Electrical Engineering"]},{"key":"dc:creator","label":"Author","values":["Jeong, Dong-Seok"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-08-13T14:40:08Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-08-13T14:40:08Z"]},{"key":"dc:date.issued","label":"Date","values":["1985"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Polytechnic Institute and State University"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.dcmitype","label":"Dc Type Dcmitype","values":["Text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/50028"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Unified approach for the early understanding of images"]}]}],"canonical_facts":{"dc:contributor.department":["Electrical Engineering"],"dc:creator":["Jeong, Dong-Seok"],"dc:date.accessioned":["2014-08-13T14:40:08Z"],"dc:date.available":["2014-08-13T14:40:08Z"],"dc:date.issued":["1985"],"dc:description.abstract":["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."],"dc:description.degree":["Master of Science"],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["http://hdl.handle.net/10919/50028"],"dc:publisher":["Virginia Polytechnic Institute and State University"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:title":["Unified approach for the early understanding of images"],"dc:type":["Thesis"],"dc:type.dcmitype":["Text"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:20:03Z"}