Architectures for Compressive Imaging with Applications in Sensor Networks, Adaptive Object Reconstruction, and Motion Detection

Persistent Link:
http://hdl.handle.net/10150/193626
Title:
Architectures for Compressive Imaging with Applications in Sensor Networks, Adaptive Object Reconstruction, and Motion Detection
Author:
Ke, Jun
Issue Date:
2010
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Computational imaging becomes a cutting edge research area by incorporating signal/image processing as an inherent part of an imaging system. Its civil and military applications include surveillance, automobile, and medical health. The newest branch of computational imaging, compressive imaging emerged in several years back. In-stead of making measurement for each individual object pixel, compressive imaging directly making compressed measurements using optical/opto-electronic devices in data acquisition process. These compressed measurements referred to as features are linear combinations of object pixels weighted by transformation bases. Usingvarious types of signal processing techniques, features are processed for the imaging system final tasks such as reconstruction, detection, and recognition. In this dissertation, three compressive imaging implementation architectures, sequential, parallel, and photon-sharing architectures, are analyzed. Two kinds of applications, object reconstruction and motion detections, are studied using projections including PC (Principal Component), Hadamard, DCT (Discrete Cosine Transformation), Gabor, and random projection. Linear and/or nonlinear algorithms are used for static and adaptive measurements. A webcam based multi-sensor network and a DMD based single detector imaging system demonstrate the dissertation work.
Type:
text; Electronic Dissertation
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Electrical & Computer Engineering; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Neifeld, Mark A.
Committee Chair:
Neifeld, Mark A.

Full metadata record

DC FieldValue Language
dc.language.isoENen_US
dc.titleArchitectures for Compressive Imaging with Applications in Sensor Networks, Adaptive Object Reconstruction, and Motion Detectionen_US
dc.creatorKe, Junen_US
dc.contributor.authorKe, Junen_US
dc.date.issued2010en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractComputational imaging becomes a cutting edge research area by incorporating signal/image processing as an inherent part of an imaging system. Its civil and military applications include surveillance, automobile, and medical health. The newest branch of computational imaging, compressive imaging emerged in several years back. In-stead of making measurement for each individual object pixel, compressive imaging directly making compressed measurements using optical/opto-electronic devices in data acquisition process. These compressed measurements referred to as features are linear combinations of object pixels weighted by transformation bases. Usingvarious types of signal processing techniques, features are processed for the imaging system final tasks such as reconstruction, detection, and recognition. In this dissertation, three compressive imaging implementation architectures, sequential, parallel, and photon-sharing architectures, are analyzed. Two kinds of applications, object reconstruction and motion detections, are studied using projections including PC (Principal Component), Hadamard, DCT (Discrete Cosine Transformation), Gabor, and random projection. Linear and/or nonlinear algorithms are used for static and adaptive measurements. A webcam based multi-sensor network and a DMD based single detector imaging system demonstrate the dissertation work.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineElectrical & Computer Engineeringen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorNeifeld, Mark A.en_US
dc.contributor.chairNeifeld, Mark A.en_US
dc.contributor.committeememberRyan, Williamen_US
dc.contributor.committeememberDallas, William J.en_US
dc.contributor.committeememberAshok, Amiten_US
dc.identifier.proquest11022en_US
dc.identifier.oclc659754973en_US
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