{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/cure-or-challenging-unreal-and-real","title":"CURE-OR: Challenging Unreal and Real Environments for Object Recognition","arxiv_id":"1810.08293","date":"2018-10-18","proceeding":null,"authors":["Dogancan Temel","Jinsol Lee","Ghassan AlRegib"],"abstract":"In this paper, we introduce a large-scale, controlled, and multi-platform\nobject recognition dataset denoted as Challenging Unreal and Real Environments\nfor Object Recognition (CURE-OR). In this dataset, there are 1,000,000 images\nof 100 objects with varying size, color, and texture that are positioned in\nfive different orientations and captured using five devices including a webcam,\na DSLR, and three smartphone cameras in real-world (real) and studio (unreal)\nenvironments. The controlled challenging conditions include underexposure,\noverexposure, blur, contrast, dirty lens, image noise, resizing, and loss of\ncolor information. We utilize CURE-OR dataset to test recognition APIs-Amazon\nRekognition and Microsoft Azure Computer Vision- and show that their\nperformance significantly degrades under challenging conditions. Moreover, we\ninvestigate the relationship between object recognition and image quality and\nshow that objective quality algorithms can estimate recognition performance\nunder certain photometric challenging conditions. The dataset is publicly\navailable at https://ghassanalregib.com/cure-or/.","url_abs":"http://arxiv.org/abs/1810.08293v2","url_pdf":"http://arxiv.org/pdf/1810.08293v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"cure-or-challenging-unreal-and-real","repo_url":"https://github.com/olivesgatech/cure-or","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"object-recognition","task_name":"Object Recognition"}],"methods":[],"datasets_introduced":[{"slug":"cure-or","name":"CURE-OR","full_name":"Challenging Unreal and Real Environments for Object Recognition"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1810.08293","atlas_url":"https://app.syntology.ai/?focus=1810.08293","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}