{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/81187"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/81187","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Mdl-Based Band Selection and Adaptive Penalties for Hyperspectral Image Segmentation","abstract":"All MRF image-segmentation criteria as in Step 3 have spatial-penalty parameters that must be chosen. An adaptive algorithm that chooses the penalty parameters to maximize the pseudo-likelihood (PL) of the current image was developed by Lakshmanan and Derin, but it uses a costly simulated-annealing algorithm. We use a decoupling argument to find simple, closed-form solutions for the PL penalty parameters of a globally adaptive (GA) MRF criterion with boundary and region penalties. A theoretical analysis shows that GA penalties only minimize the error rate if the scene has certain weak symmetry properties. For example, all boundaries must be equally rough. This is not always satisfied in practice, so we also introduce an MRF with class-pair-conditional (CP) boundary penalties. We segment both synthetic and real images to validate the theoretical analysis and illustrate the capabilities and limitations inherent to the PL approximation.","abstract_html":"All MRF image-segmentation criteria as in Step 3 have spatial-penalty parameters that must be chosen. An adaptive algorithm that chooses the penalty parameters to maximize the pseudo-likelihood (PL) of the current image was developed by Lakshmanan and Derin, but it uses a costly simulated-annealing algorithm. We use a decoupling argument to find simple, closed-form solutions for the PL penalty parameters of a globally adaptive (GA) MRF criterion with boundary and region penalties. A theoretical analysis shows that GA penalties only minimize the error rate if the scene has certain weak symmetry properties. For example, all boundaries must be equally rough. This is not always satisfied in practice, so we also introduce an MRF with class-pair-conditional (CP) boundary penalties. We segment both synthetic and real images to validate the theoretical analysis and illustrate the capabilities and limitations inherent to the PL approximation.","abstract_has_math":false,"creators":["Kerfoot, Ian B."],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical Engineering","degree_department":null,"school":null,"contributors":["Bresler, Yoram"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T20:09:59Z","date_published":"2015-09-25T20:09:59Z","updated_at":"2026-07-22T22:26:15Z","subjects":["Engineering, Electronics and Electrical"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI9737156"],"render_values":[{"text":"(MiAaPQ)AAI9737156","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/81187","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bresler, Yoram"]},{"key":"dc:creator","label":"Author","values":["Kerfoot, Ian B."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T20:09:59Z","10000-01-01","1997"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Engineering, Electronics and Electrical"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/81187","(MiAaPQ)AAI9737156"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["All MRF image-segmentation criteria as in Step 3 have spatial-penalty parameters that must be chosen. An adaptive algorithm that chooses the penalty parameters to maximize the pseudo-likelihood (PL) of the current image was developed by Lakshmanan and Derin, but it uses a costly simulated-annealing algorithm. We use a decoupling argument to find simple, closed-form solutions for the PL penalty parameters of a globally adaptive (GA) MRF criterion with boundary and region penalties. A theoretical analysis shows that GA penalties only minimize the error rate if the scene has certain weak symmetry properties. For example, all boundaries must be equally rough. This is not always satisfied in practice, so we also introduce an MRF with class-pair-conditional (CP) boundary penalties. We segment both synthetic and real images to validate the theoretical analysis and illustrate the capabilities and limitations inherent to the PL approximation.","Made available in DSpace on 2015-09-25T20:09:59Z (GMT). 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An adaptive algorithm that chooses the penalty parameters to maximize the pseudo-likelihood (PL) of the current image was developed by Lakshmanan and Derin, but it uses a costly simulated-annealing algorithm. We use a decoupling argument to find simple, closed-form solutions for the PL penalty parameters of a globally adaptive (GA) MRF criterion with boundary and region penalties. A theoretical analysis shows that GA penalties only minimize the error rate if the scene has certain weak symmetry properties. For example, all boundaries must be equally rough. This is not always satisfied in practice, so we also introduce an MRF with class-pair-conditional (CP) boundary penalties. We segment both synthetic and real images to validate the theoretical analysis and illustrate the capabilities and limitations inherent to the PL approximation.","Made available in DSpace on 2015-09-25T20:09:59Z (GMT). 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