Osa Hernández, Borja de la

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https://academica-e.unavarra.es/handle/2454/49701

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Osa Hernández

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Borja de la

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Estadística, Informática y Matemáticas

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751850

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811826

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2020 - 20213
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  • Thumbnail Image
    PublicationOpen Access
    Enhancing the efficiency of the interval-valued fuzzy rule-based classifier with tuning and rule selection
    (Springer, 2020) Sanz Delgado, José Antonio; Da Cruz Asmus, Tiago; Osa Hernández, Borja de la; Bustince Sola, Humberto; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa, PJUPNA1926
    Interval-Valued fuzzy rule-based classifier with TUning and Rule Selection, IVTURS, is a state-of-the-art fuzzy classifier. One of the key point of this method is the usage of interval-valued restricted equivalence functions because their parametrization allows one to tune them to each problem, which leads to obtaining accurate results. However, they require the application of the exponentiation several times to obtain a result, which is a time demanding operation implying an extra charge to the computational burden of the method. In this contribution, we propose to reduce the number of exponentiation operations executed by the system, so that the efficiency of the method is enhanced with no alteration of the obtained results. Moreover, the new approach also allows for a reduction on the search space of the evolutionary method carried out in IVTURS. Consequently, we also propose four different approaches to take advantage of this reduction on the search space to study if it can imply an enhancement of the accuracy of the classifier. The experimental results prove: 1) the enhancement of the efficiency of IVTURS and 2) the accuracy of IVTURS is competitive versus that of the approaches using the reduced search space.
  • Thumbnail Image
    PublicationOpen 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 TFIPDL
    Classic 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.
  • Thumbnail Image
    PublicationOpen 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 Matematika
    Image 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.