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dc.contributor.authorMurray-Bruce, Johnen_US
dc.contributor.authorSaunders, Charlesen_US
dc.contributor.authorGoyal, Vivek K.en_US
dc.date.accessioned2020-05-01T13:38:32Z
dc.date.available2020-05-01T13:38:32Z
dc.date.issued2019-09-09
dc.identifier.citationJohn Murray-Bruce, Charles Saunders, Vivek K Goyal. 2019. "Occlusion-based computational periscopy with consumer cameras." Wavelets and Sparsity XVIII. Wavelets and Sparsity XVIII. 2019-08-11 - 2019-08-15. https://doi.org/10.1117/12.2528322
dc.identifier.urihttps://hdl.handle.net/2144/40494
dc.description.abstractThe ability to form images of scenes hidden from direct view would be advantageous in many applications – from improved motion planning and collision avoidance in autonomous navigation to enhanced danger anticipation for first-responders in search-and-rescue missions. Recent techniques for imaging around corners have mostly relied on time-of-flight measurements of light propagation, necessitating the use of expensive, specialized optical systems. In this work, we demonstrate how to form images of hidden scenes from intensity-only measurements of the light reaching a visible surface from the hidden scene. Our approach exploits the penumbra cast by an opaque occluding object onto a visible surface. Specifically, we present a physical model that relates the measured photograph to the radiosity of the hidden scene and the visibility function due to the opaque occluder. For a given scene–occluder setup, we characterize the parts of the hidden region for which the physical model is well-conditioned for inversion – i.e., the computational field of view (CFOV) of the imaging system. This concept of CFOV is further verified through the Cram´er–Rao bound of the hidden-scene estimation problem. Finally, we present a two-step computational method for recovering the occluder and the scene behind it. We demonstrate the effectiveness of the proposed method using both synthetic and experimentally measured data.en_US
dc.language.isoen_US
dc.publisherSPIEen_US
dc.relation.ispartofWavelets and Sparsity XVIII
dc.subjectComputational periscopyen_US
dc.subjectNon-line-of-sight imagingen_US
dc.subjectComputational photographyen_US
dc.subjectComputer visionen_US
dc.subjectRemote sensingen_US
dc.titleOcclusion-based computational periscopy with consumer camerasen_US
dc.typeConference materialsen_US
dc.description.versionAccepted manuscripten_US
dc.identifier.doi10.1117/12.2528322
pubs.elements-sourcecrossrefen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Engineeringen_US
pubs.organisational-groupBoston University, College of Engineering, Department of Electrical & Computer Engineeringen_US
pubs.publication-statusPublisheden_US
dc.identifier.mycv527988


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