Compressive Hyperspectral Structured Illumination and Classification via Neural Networks

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dc.contributor.advisorKelly, Kevin F.en_US
dc.creatorXu, Yiboen_US
dc.date.accessioned2017-08-03T17:10:22Zen_US
dc.date.available2017-08-03T17:10:22Zen_US
dc.date.created2016-05en_US
dc.date.issued2016-04-21en_US
dc.date.submittedMay 2016en_US
dc.date.updated2017-08-03T17:10:22Zen_US
dc.description.abstractWe demonstrate two complementary applications based on compressive imaging: hyperspectral compressive structured illumination for three-dimensional imaging and compressive classification of objects using neural networks. The structured light method usually uses structured patterns generated from a commercial digital projector which contain very limited spectral content, using white light or RGB-based giving very little material content and not exploiting possible wavelength-dependent scattering. Therefore we designed and implemented a hyperspectral projector system that is able to generate structured patterns consisting of arbitrarily defined spectrum instead. We used the system to recover the unique spectrum-dependent 3-D volume density of the colored targets of participating media. For the image classification problem, it is known that a set of images of a fixed scene under varying articulation parameters forms a low-dimensional, nonlinear manifold that random projections can stably embed using far fewer measurements. Thus random projections in compressive sampling can be regarded as a dimension-reducing process. We demonstrate a method using compressive measurements of images to train a neural network that has a relatively simple architecture for object classification. As a proof of concept, simulations were performed on infrared vehicle images that demonstrated the utility of this approach over previous compressive matched filtering. The success of both these projects bodes well for their overall integration into a single infrared compressive hyperspectral machine-vision instrument.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationXu, Yibo. "Compressive Hyperspectral Structured Illumination and Classification via Neural Networks." (2016) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/96559">https://hdl.handle.net/1911/96559</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/96559en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectcompressive sensingen_US
dc.subjectstructured lighten_US
dc.subjectvolume densityen_US
dc.subjecthyperspectral projectoren_US
dc.subjectneural networken_US
dc.subjectclassificationen_US
dc.titleCompressive Hyperspectral Structured Illumination and Classification via Neural Networksen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentElectrical and Computer Engineeringen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Scienceen_US
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