{"id":{"repo_id":"york","oai_identifier":"oai:yorkspace.library.yorku.ca:10315/41869"},"canonical_url":"https://search.dev.ndltd.org/etd/york/oai:yorkspace.library.yorku.ca:10315/41869","repository":{"repo_id":"york","name":"York University","base_url":"https://yorkspace.library.yorku.ca/oai/request"},"display":{"title":"Examining Autoexposure for Challenging Scenes","abstract":"Autoexposure (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.","abstract_html":"Autoexposure (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.","abstract_has_math":false,"creators":["Yang, Beixuan"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Brown, Michael S."],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-03-16","date_published":"2024-03-16","updated_at":"2026-07-24T06:33:38Z","subjects":["Computer science"],"languages":["en"],"rights":["Author owns copyright, except where explicitly noted. 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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."]},{"key":"dc:title","label":"Title","values":["Examining Autoexposure for Challenging Scenes"]}]}],"canonical_facts":{"dc:contributor.advisor":["Brown, Michael S."],"dc:creator":["Yang, Beixuan"],"dc:date.accessioned":["2024-03-18T17:54:24Z"],"dc:date.available":["2024-03-18T17:54:24Z"],"dc:date.issued":["2024-03-16"],"dc:description.abstract":["Autoexposure (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.uri":["https://hdl.handle.net/10315/41869"],"dc:language":["en"],"dc:rights":["Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests."],"dc:subject":["Computer science"],"dc:title":["Examining Autoexposure for Challenging Scenes"],"dc:type":["Electronic Thesis or Dissertation"]},"updated_at":"2026-07-24T06:33:38Z"}