Feature-Specific Imaging: Extensions to Adaptive Object Recognition and Active Illumination Based Scene Reconstruction

Persistent Link:
http://hdl.handle.net/10150/193587
Title:
Feature-Specific Imaging: Extensions to Adaptive Object Recognition and Active Illumination Based Scene Reconstruction
Author:
Baheti, Pawan Kumar
Issue Date:
2008
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 (CI) systems are hybrid imagers in which the optical and post-processing sub-systems are jointly optimized to maximize the task-specific performance. In this dissertation we consider a form of CI system that measures the linear projections (i.e., features) of the scene optically, and it is commonly referred to as feature-specific imaging (FSI). Most of the previous work on FSI has been concerned with image reconstruction. Previous FSI techniques have also been non-adaptive and restricted to the use of ambient illumination.We consider two novel extensions of the FSI system in this work. We first present an adaptive feature-specific imaging (AFSI) system and consider its application to a face-recognition task. The proposed system makes use of previous measurements to adapt the projection basis at each step. We present both statistical and information-theoretic adaptation mechanisms for the AFSI system. The sequential hypothesis testing framework is used to determine the number of measurements required for achieving a specified misclassification probability. We demonstrate that AFSI system requires significantly fewer measurements than static-FSI (SFSI) and conventional imaging at low signal-to-noise ratio (SNR). We also show a trade-off, in terms of average detection time, between measurement SNR and adaptation advantage. Experimental results validating the AFSI system are presented.Next we present a FSI system based on the use of structured light. Feature measurements are obtained by projecting spatially structured illumination onto an object and collecting all of the reflected light onto a single photodetector. We refer to this system as feature-specific structured imaging (FSSI). Principal component features are used to define the illumination patterns. The optimal LMMSE operator is used to generate object estimates from the measurements. We demonstrate that this new imaging approach reduces imager complexity and provides improved image quality in high noise environments. We then generalize the FSSI system by making use of random projections (i.e., using no object prior) to define the illumination patterns. Object estimates are generated using L1-norm minimization and gradient-projection sparse reconstruction algorithms. Experimental results validating the FSSI system are presented.
Type:
text; Electronic Dissertation
Keywords:
Computational Imaging; Information theory
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Electrical & Computer Engineering; Graduate College
Degree Grantor:
University of Arizona
Committee Chair:
Neifeld, Mark A.

Full metadata record

DC FieldValue Language
dc.language.isoENen_US
dc.titleFeature-Specific Imaging: Extensions to Adaptive Object Recognition and Active Illumination Based Scene Reconstructionen_US
dc.creatorBaheti, Pawan Kumaren_US
dc.contributor.authorBaheti, Pawan Kumaren_US
dc.date.issued2008en_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 (CI) systems are hybrid imagers in which the optical and post-processing sub-systems are jointly optimized to maximize the task-specific performance. In this dissertation we consider a form of CI system that measures the linear projections (i.e., features) of the scene optically, and it is commonly referred to as feature-specific imaging (FSI). Most of the previous work on FSI has been concerned with image reconstruction. Previous FSI techniques have also been non-adaptive and restricted to the use of ambient illumination.We consider two novel extensions of the FSI system in this work. We first present an adaptive feature-specific imaging (AFSI) system and consider its application to a face-recognition task. The proposed system makes use of previous measurements to adapt the projection basis at each step. We present both statistical and information-theoretic adaptation mechanisms for the AFSI system. The sequential hypothesis testing framework is used to determine the number of measurements required for achieving a specified misclassification probability. We demonstrate that AFSI system requires significantly fewer measurements than static-FSI (SFSI) and conventional imaging at low signal-to-noise ratio (SNR). We also show a trade-off, in terms of average detection time, between measurement SNR and adaptation advantage. Experimental results validating the AFSI system are presented.Next we present a FSI system based on the use of structured light. Feature measurements are obtained by projecting spatially structured illumination onto an object and collecting all of the reflected light onto a single photodetector. We refer to this system as feature-specific structured imaging (FSSI). Principal component features are used to define the illumination patterns. The optimal LMMSE operator is used to generate object estimates from the measurements. We demonstrate that this new imaging approach reduces imager complexity and provides improved image quality in high noise environments. We then generalize the FSSI system by making use of random projections (i.e., using no object prior) to define the illumination patterns. Object estimates are generated using L1-norm minimization and gradient-projection sparse reconstruction algorithms. Experimental results validating the FSSI system are presented.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjectComputational Imagingen_US
dc.subjectInformation theoryen_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.chairNeifeld, Mark A.en_US
dc.contributor.committeememberMarcelin, Michael W.en_US
dc.contributor.committeememberGoodman, Nathanen_US
dc.contributor.committeememberKostuk, Raymonden_US
dc.identifier.proquest10096en_US
dc.identifier.oclc659750509en_US
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