Simulating weather conditions on digital images

A tétel rövid nézete
dc.contributor.advisorHajdú, András
dc.contributor.authorZaher, Ghais
dc.contributor.departmentDE--Informatikai Karhu_HU
dc.date.accessioned2020-05-08T10:35:57Z
dc.date.available2020-05-08T10:35:57Z
dc.date.created2020-05
dc.description.abstractObject detection is an essential part of Autonomous Driving and Driver-Assistance systems. Nevertheless, bad weather conditions like fog, rain and smog decrease the ability to understand the surrounding scene, just like human vision can be impaired in such situations. To overcome this challenge, we simulate fog on clear images that are already annotated then systems are trained to recognize objects using such dataset. In this thesis, we use Convolutional Neural Networks to generate fog on clear images, reducing the human effort of finding mathematical foggificaion formulas and leting our model figure that out. We propose a generative model that simulates fog on clear images using an unpaired dataset of both foggy and fog-free images.hu_HU
dc.description.correctorN.I.
dc.description.courseComputer Sciencehu_HU
dc.description.degreeMSc/MAhu_HU
dc.format.extent51hu_HU
dc.identifier.urihttp://hdl.handle.net/2437/286375
dc.language.isoenhu_HU
dc.subjectFoghu_HU
dc.subjectWeatherhu_HU
dc.subjectAutonomous-Drivinghu_HU
dc.subjectSimulationhu_HU
dc.subject.dspaceDEENK Témalista::Informatikahu_HU
dc.titleSimulating weather conditions on digital imageshu_HU
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