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dc.contributor.advisorSturgill, David Brian.
dc.contributor.authorVan Ruitenbeek, Benjamin D.
dc.contributor.otherBaylor University. Dept. of Computer Science.en
dc.date.accessioned2009-08-25T16:32:33Z
dc.date.available2009-08-25T16:32:33Z
dc.date.copyright2009-08
dc.date.issued2009-08-25T16:32:33Z
dc.identifier.urihttp://hdl.handle.net/2104/5397
dc.descriptionIncludes bibliographical references (p. 71-73).en
dc.description.abstractWe present a novel method for sparse signal recovery using Particle Swarm Optimization and demonstrate an application in image compression. Images are compressed with compressive sampling, and then reconstructed with particle swarm techniques. Several enhancements to the basic particle swarm algorithm are shown to improve signal recovery accuracy. We also present techniques specifically for reconstructing sparse image data and evaluate their performance.en
dc.description.statementofresponsibilityby Benjamin D. Van Ruitenbeek.en
dc.format.extentvii, 73 p. : ill.en
dc.format.extent702104 bytes
dc.format.extent511205 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen
dc.rightsBaylor University 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. Contact librarywebmaster@baylor.edu for inquiries about permission.en
dc.subjectImage compression.en
dc.subjectMathematical optimization -- Computer programs.en
dc.subjectSwarm intelligence.en
dc.titleImage compression and recovery using compressive sampling and particle swarm optimization.en
dc.typeThesisen
dc.description.degreeM.S.en
dc.rights.accessrightsWorldwide accessen
dc.contributor.departmentComputer Science.en


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