{"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/fast-and-robust-multiple-colorchecker","title":"Fast and Robust Multiple ColorChecker Detection using Deep Convolutional Neural Networks","arxiv_id":"1810.08639","date":"2018-10-19","proceeding":null,"authors":["Pedro D. Marrero Fernandez","Fidel A. Guerrero-Peña","Tsang Ing Ren","Jorge J. G. Leandro"],"abstract":"ColorCheckers are reference standards that professional photographers and\nfilmmakers use to ensure predictable results under every lighting condition.\nThe objective of this work is to propose a new fast and robust method for\nautomatic ColorChecker detection. The process is divided into two steps: (1)\nColorCheckers localization and (2) ColorChecker patches recognition. For the\nColorChecker localization, we trained a detection convolutional neural network\nusing synthetic images. The synthetic images are created with the 3D models of\nthe ColorChecker and different background images. The output of the neural\nnetworks are the bounding box of each possible ColorChecker candidates in the\ninput image. Each bounding box defines a cropped image which is evaluated by a\nrecognition system, and each image is canonized with regards to color and\ndimensions. Subsequently, all possible color patches are extracted and grouped\nwith respect to the center's distance. Each group is evaluated as a candidate\nfor a ColorChecker part, and its position in the scene is estimated. Finally, a\ncost function is applied to evaluate the accuracy of the estimation. The method\nis tested using real and synthetic images. The proposed method is fast, robust\nto overlaps and invariant to affine projections. The algorithm also performs\nwell in case of multiple ColorCheckers detection.","url_abs":"http://arxiv.org/abs/1810.08639v1","url_pdf":"http://arxiv.org/pdf/1810.08639v1.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":"fast-and-robust-multiple-colorchecker","repo_url":"https://github.com/pedrodiamel/colorchacker-detection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"fast-and-robust-multiple-colorchecker","repo_url":"https://github.com/pedrodiamel/colorchecker-detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}