{"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/sugeno-integral-generalization-applied-to","title":"Sugeno integral generalization applied to improve adaptive image binarization","arxiv_id":null,"date":"2020-10-20","proceeding":"Information Fusion 2020 10","authors":["Bardozzo Francesco","De La Osa Borja","Horansk{\\'a}","L'ubom{\\'\\i}ra","Fumanal-Idocin Javier","delli Priscoli  Mattia","Troiano  Luigi","Tagliaferri Roberto","Fernandez Javier","Bustince Humberto"],"abstract":"Classic adaptive binarization methodologies threshold pixels intensity with re-spect to adjacent pixels exploiting integral images. In turn, integral imagesare generally computed optimally by using the summed-area-table algorithm(SAT). This document presents a new adaptive binarization technique basedon fuzzy integral images. Which, in turn, this technique is supported by anefficient design of a modified SAT for generalized Sugeno fuzzy integrals. Wedefine this methodology as FLAT (Fuzzy Local Adaptive Thresholding). Exper-imental results show that the proposed methodology produced a better imagequality thresholding than well-known global and local thresholding algorithms.We proposed new generalizations of different fuzzy integrals to improve existingresults and reaching an accuracy≈0.94 on a wide dataset. Moreover, due tohigh performances, these new generalized Sugeno fuzzy integrals created ad hocfor adaptive binarization, can be used as tools for grayscale processing and morecomplex real-time thresholding applications.","url_abs":"https://www.sciencedirect.com/science/article/pii/S1566253520304012","url_pdf":"https://www.sciencedirect.com/science/article/pii/S1566253520304012","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":"sugeno-integral-generalization-applied-to","repo_url":"https://github.com/lodeguns/FuzzyAdaptiveBinarization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"binarization","task_name":"Binarization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}