Examining Autoexposure for Challenging Scenes

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dc.contributor.advisorBrown, Michael S.
dc.contributor.authorYang, Beixuan
dc.date.accessioned2024-03-18T17:54:24Z
dc.date.available2024-03-18T17:54:24Z
dc.date.issued2024-03-16
dc.date.updated2024-03-16T10:52:09Z
dc.degree.disciplineComputer Science
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractAutoexposure (AE) is a critical step cameras apply to ensure properly exposed images. While current AE algorithms are effective in well-lit environments with unchanging illumination, these algorithms still struggle in environments with bright light sources or scenes with abrupt changes in lighting. A significant hurdle in developing new AE algorithms for challenging environments, especially those with time-varying lighting, is the lack of platforms to evaluate AE algorithms and suitable image datasets. To address this issue, we have designed a software platform allowing AE algorithms to be used in a plug-and-play manner with the dataset. In addition, we have captured a new 4D exposure dataset that provides a complete solution space (i.e., all possible exposures) over a temporal sequence with moving objects, bright lights, and varying lighting. Our dataset and associate platform enable repeatable evaluation of different AE algorithms and provide a much-needed starting point to develop better AE methods.
dc.identifier.urihttps://hdl.handle.net/10315/41869
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectComputer science
dc.subject.keywordsAuto exposure
dc.subject.keywordsComputer science
dc.subject.keywordsComputer vision
dc.subject.keywordsImage processing
dc.subject.keywordsImage dataset
dc.subject.keywordsCamera pipeline
dc.titleExamining Autoexposure for Challenging Scenes
dc.typeElectronic Thesis or Dissertation

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