Fumanal Idocin, Javier
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Fumanal Idocin
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Javier
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Estadística, Informática y Matemáticas
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Publication Open Access Sugeno integral generalization applied to improve adaptive image binarization(Elsevier, 2021) Bardozzo, Francesco; Osa Hernández, Borja de la; Horanská, Lubomíra; Fumanal Idocin, Javier; Priscoli, Mattia delli; Troiano, Luigi; Tagliaferri, Roberto; Fernández Fernández, Francisco Javier; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Gobierno de Navarra / Nafarroako Gobernua, PI043-2019; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa, PC093-094 TFIPDLClassic adaptive binarization methodologies threshold pixels intensity with respect to adjacent pixels exploiting integral images. In turn, integral images are generally computed optimally by using the summed-area-table algorithm (SAT). This document presents a new adaptive binarization technique based on fuzzy integral images. Which, in turn, this technique is supported by an efficient design of a modified SAT for generalized Sugeno fuzzy integrals. We define this methodology as FLAT (Fuzzy Local Adaptive Thresholding). Experimental results show that the proposed methodology produced a better image quality thresholding than well-known global and local thresholding algorithms. We proposed new generalizations of different fuzzy integrals to improve existing results and reaching an accuracy ≈0.94 on a wide dataset. Moreover, due to high performances, these new generalized Sugeno fuzzy integrals created ad hoc for adaptive binarization, can be used as tools for grayscale processing and more complex real-time thresholding applications.Publication Open Access Gated local adaptive binarization using supervised learning(CEUR Workshop Proceedings (CEUR-WS.org), 2021) Fumanal Idocin, Javier; Uriarte Barragán, Juan; Osa Hernández, Borja de la; Bardozzo, Francesco; Fernández Fernández, Francisco Javier; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta MatematikaImage thresholding is one of the most popular problems in image processing. However, changes inlightning and contrast in an image can cause trouble for the existing algorithms that use a global threshold for all the image. A solution for this problem is the adaptive thresholding, in which an image canhave different thresholds for different parts of the image. Yet, the problem of choosing the most suitable threshold for each region of the image is still open. In this paper we present the Gated Local Adaptive Binarization algorithm, in which we choose the most appropriate threshold for each region of the image using a logistic regression. Our results show that this algorithm can effectively learn the most appropriate threshold in each situation, and beats other adaptive binarization solutions for a standard dataset in the literature.