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dc.contributor.advisorRamesh Raskar.en_US
dc.contributor.authorSaxena, Alishaen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2018-12-18T19:47:01Z
dc.date.available2018-12-18T19:47:01Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/119716
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (page 51).en_US
dc.description.abstractIdentifying objects through fog is an important problem that is difficult even for the human eye. Solving this problem would make autonomous vehicles, drones, and other similar systems more resilient to changing natural weather conditions. While there are existing solutions for dehazing images occluded by light fog, these solutions are not effective in cases of very dense fog. Hence, we present a system that uses a combination of time resolved sensing, specifically using Single Photon Avalanche Photodiode (SPAD) cameras, and deep learning with convolutional neural networks to detect and classify objects when imaged through extreme scattering media like fog. This thesis describes our three-pronged approach to solving this problem: (1) building simulation software to gather sufficient training data, (2) verifying and benchmarking output of simulation with real-life fog data, (3) training deep learning models to classify objects occluded by fog.en_US
dc.description.statementofresponsibilityby Alisha Saxena.en_US
dc.format.extent51 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleA data-driven approach to object classification through fogen_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc1078637047en_US


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