{"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/an-adaptive-fuzzy-based-system-to-simulate","title":"An Adaptive Fuzzy-Based System to Simulate, Quantify and Compensate Color Blindness","arxiv_id":"1711.10662","date":"2017-11-29","proceeding":null,"authors":["Jinmi Lee","Wellington Pinheiro dos Santos"],"abstract":"About 8% of the male population of the world are affected by a determined\ntype of color vision disturbance, which varies from the partial to complete\nreduction of the ability to distinguish certain colors. A considerable amount\nof color blind people are able to live all life long without knowing they have\ncolor vision disabilities and abnormalities. Nowadays the evolution of\ninformation technology and computer science, specifically image processing\ntechniques and computer graphics, can be fundamental to aid at the development\nof adaptive color blindness correction tools. This paper presents a software\ntool based on Fuzzy Logic to evaluate the type and the degree of color\nblindness a person suffer from. In order to model several degrees of color\nblindness, herein this work we modified the classical linear transform-based\nsimulation method by the use of fuzzy parameters. We also proposed four new\nmethods to correct color blindness based on a fuzzy approach: Methods A and B,\nwith and without histogram equalization. All the methods are based on\ncombinations of linear transforms and histogram operations. In order to\nevaluate the results we implemented a web-based survey to get the best results\naccording to optimize to distinguish different elements in an image. Results\nobtained from 40 volunteers proved that the Method B with histogram\nequalization got the best results for about 47% of volunteers.","url_abs":"http://arxiv.org/abs/1711.10662v1","url_pdf":"http://arxiv.org/pdf/1711.10662v1.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":"an-adaptive-fuzzy-based-system-to-simulate","repo_url":"https://github.com/N-thon/ColourBlindConverter","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}