The smashed filter for compressive classification and target recognition

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dc.citation.bibtexNameinproceedingsen_US
dc.citation.journalTitleComputational Imaging V at SPIE Electronic Imagingen_US
dc.citation.locationSan Jose, Californiaen_US
dc.contributor.authorDavenport, Mark A.en_US
dc.contributor.authorDuarte, Marco F.en_US
dc.contributor.authorWakin, Michael B.en_US
dc.contributor.authorLaska, Jason N.en_US
dc.contributor.authorTakhar, Dharmpalen_US
dc.contributor.authorKelly, Kevin F.en_US
dc.contributor.authorBaraniuk, Richard G.en_US
dc.date.accessioned2008-08-19T02:13:37Zen_US
dc.date.available2008-08-19T02:13:37Zen_US
dc.date.issued2007-01-01en_US
dc.description.abstractThe theory of compressive sensing (CS) enables the reconstruction of a sparse or compressible image or signal from a small set of linear, non-adaptive (even random) projections. However, in many applications, including object and target recognition, we are ultimately interested in making a decision about an image rather than computing a reconstruction. We propose here a framework for compressive classification that operates directly on the compressive measurements without first reconstructing the image. We dub the resulting dimensionally reduced matched filter the smashed filter. The first part of the theory maps traditional maximum likelihood hypothesis testing into the compressive domain; we find that the number of measurements required for a given classification performance level does not depend on the sparsity or compressibility of the images but only on the noise level. The second part of the theory applies the generalized maximum likelihood method to deal with unknown transformations such as the translation, scale, or viewing angle of a target object. We exploit the fact the set of transformed images forms a low-dimensional, nonlinear manifold in the high-dimensional image space. We find that the number of measurements required for a given classification performance level grows linearly in the dimensionality of the manifold but only logarithmically in the number of pixels/samples and image classes. Using both simulations and measurements from a new single-pixel compressive camera, we demonstrate the effectiveness of the smashed filter for target classification using very few measurements.en_US
dc.description.sponsorshipThis work was supported by the grants DARPA/ONR N66001-06-1-2011 and N00014-06-1-0610, NSF CCF-0431150, NSF DMS-0603606, ONR N00014-06-1-0769 and N00014-06-1-0829, AFOSR FA9550-04-1- 0148, and the Texas Instruments Leadership University Program. Thanks to TI for providing the DMD developer’s kit and accessory light modulator package (ALP).en_US
dc.identifier.citationM. A. Davenport, M. F. Duarte, M. B. Wakin, J. N. Laska, D. Takhar, K. F. Kelly and R. G. Baraniuk, "The smashed filter for compressive classification and target recognition," <i>Computational Imaging V at SPIE Electronic Imaging,</i> 2007.en_US
dc.identifier.doihttp://dx.doi.org/10.1117/12.714460en_US
dc.identifier.urihttps://hdl.handle.net/1911/21679en_US
dc.language.isoengen_US
dc.subjectsmashed filteren_US
dc.subjectobject recognitionen_US
dc.subjectimage classificationen_US
dc.subjectcompressive sensingen_US
dc.titleThe smashed filter for compressive classification and target recognitionen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.dcmiTexten_US
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