Person:
Barrenechea Tartas, Edurne

Last Name

Barrenechea Tartas

First Name

Edurne

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Estadística, Informática y Matemáticas

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ISC. Institute of Smart Cities

ORCID

0000-0001-6657-948X

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2578

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Now showing 1 - 10 of 23

Open Access
A survey of fingerprint classification Part II: experimental analysis and ensemble proposal
(Elsevier, 2015) Galar Idoate, Mikel; Derrac, Joaquín; Peralta, Daniel; Triguero, Isaac; Paternain Dallo, Daniel; López Molina, Carlos; García, Salvador; Benítez, José Manuel; Pagola Barrio, Miguel; Barrenechea Tartas, Edurne; Bustince Sola, Humberto; Herrera, Francisco; Automática y Computación; Automatika eta Konputazioa
In the first part of this paper we reviewed the fingerprint classification literature from two different perspectives: the feature extraction and the classifier learning. Aiming at answering the question of which among the reviewed methods would perform better in a real implementation we end up in a discussion which showed the difficulty in answering this question. No previous comparison exists in the literature and comparisons among papers are done with different experimental frameworks. Moreover, the difficulty in implementing published methods was stated due to the lack of details in their description, parameters and the fact that no source code is shared. For this reason, in this paper we will go through a deep experimental study following the proposed double perspective. In order to do so, we have carefully implemented some of the most relevant feature extraction methods according to the explanations found in the corresponding papers and we have tested their performance with different classifiers, including those specific proposals made by the authors. Our aim is to develop an objective experimental study in a common framework, which has not been done before and which can serve as a baseline for future works on the topic. This way, we will not only test their quality, but their reusability by other researchers and will be able to indicate which proposals could be considered for future developments. Furthermore, we will show that combining different feature extraction models in an ensemble can lead to a superior performance, significantly increasing the results obtained by individual models.
Open Access
Cuantificar los hechos represivos: explicación y retos de la base de datos del fondo documental de la memoria histórica en Navarra
(2019) Majuelo Gil, Emilio; Mendiola Gonzalo, Fernando; Garmendia Amutxastegi, Gotzon; Piérola Narvarte, Gemma; García Funes, Juan Carlos; Yániz Berrio, Edurne; Pérez Ibarrola, Nerea; Barrenechea Tartas, Edurne; Rodríguez Martínez, Iosu; Sesma Redín, Rubén; Bustince Sola, Humberto; Ciencias Humanas y de la Educación; Giza eta Hezkuntza Zientziak; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
En este documento se presentan los fundamentos historiográficos y metodológicos de la base de datos del Fondo Documental de la Memoria Histórica en Navarra, desarrollada en la Universidad Pública de Navarra como consecuencia del encargo institucional realizado por el Parlamento y el Gobierno de Navarra. Con este fin, se ha procedido a elaborar una base de datos que permita una ágil consulta por parte de diferentes agentes sociales, institucionales y académicos en torno a la represión franquista, intentando incluir en ella la gran variedad de prácticas represivas que la historiografía ha ido identificando. Primeramente, se presenta un balance sobre la publicación, en los últimos años, de diferentes bases de datos on-line en torno a las víctimas de la guerra civil y la represión franquista en varias comunidades autónomas. A continuación, se presenta la unidad de análisis de nuestra base de datos, “los hechos represivos”, insertándola en el contexto historiográfico en torno a la represión franquista y los estudios sobre la violencia. En un tercer apartado pasamos a describir las diferentes categorías y subcategorías represivas en las que se enmarcan los hechos represivos, y finalmente se presentan algunas características técnicas de la organización interna de la información y el sofware de la base de datos.
Open Access
Strengthened ordered directional and other generalizations of monotonicity for aggregation functions
(Springer, 2018) Sesma Sara, Mikel; Miguel Turullols, Laura de; Lafuente López, Julio; Barrenechea Tartas, Edurne; Mesiar, Radko; Bustince Sola, Humberto; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Estadística, Informática y Matemáticas; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
A tendency in the theory of aggregation functions is the generalization of the monotonicity condition. In this work, we examine the latest developments in terms of different generalizations. In particular, we discuss strengthened ordered directional monotonicity, its relation to other types of monotonicity, such as directional and ordered directional monotonicity and the main properties of the class of functions that are strengthened ordered directionally monotone. We also study some construction methods for such functions and provide a characterization of usual monotonicity in terms of these notions of monotonicity.
Open Access
Using the Choquet integral in the fuzzy reasoning method of fuzzy rule-based classification systems
(MDPI, 2013) Barrenechea Tartas, Edurne; Bustince Sola, Humberto; Fernández Fernández, Francisco Javier; Paternain Dallo, Daniel; Sanz Delgado, José Antonio; Automática y Computación; Automatika eta Konputazioa
In this paper we present a new fuzzy reasoning method in which the Choquet integral is used as aggregation function. In this manner, we can take into account the interaction among the rules of the system. For this reason, we consider several fuzzy measures, since it is a key point on the subsequent success of the Choquet integral, and we apply the new method with the same fuzzy measure for all the classes. However, the relationship among the set of rules of each class can be different and therefore the best fuzzy measure can change depending on the class. Consequently, we propose a learning method by means of a genetic algorithm in which the most suitable fuzzy measure for each class is computed. From the obtained results it is shown that our new proposal allows the performance of the classical fuzzy reasoning methods of the winning rule and additive combination to be enhanced whenever the fuzzy measure is appropriate for the tackled problem.
Open Access
Learning ordered pooling weights in image classification
(Elsevier, 2020) Forcén Carvalho, Juan Ignacio; Pagola Barrio, Miguel; Barrenechea Tartas, Edurne; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Spatial pooling is an important step in computer vision systems like Convolutional Neural Networks or the Bag-of-Words method. The spatial pooling purpose is to combine neighbouring descriptors to obtain a single descriptor for a given region (local or global). The resultant combined vector must be as discriminant as possible, in other words, must contain relevant information, while removing irrelevant and confusing details. Maximum and average are the most common aggregation functions used in the pooling step. To improve the aggregation of relevant information without degrading their discriminative power for image classification, we introduce a simple but effective scheme based on Ordered Weighted Average (OWA) aggregation operators. We present a method to learn the weights of the OWA aggregation operator in a Bag-of-Words framework and in Convolutional Neural Networks, and provide an extensive evaluation showing that OWA based pooling outperforms classical aggregation operators.
Open Access
Interval type-2 fuzzy sets are generalization of interval-valued fuzzy sets: towards a wider view on their relationship
(IEEE, 2015) Bustince Sola, Humberto; Fernández Fernández, Francisco Javier; Hagras, Hani; Herrera, Francisco; Pagola Barrio, Miguel; Barrenechea Tartas, Edurne; Automática y Computación; Automatika eta Konputazioa
In this paper, we will present a wider view on the relationship between interval-valued fuzzy sets and interval type- 2 fuzzy sets where we will show that interval-valued fuzzy sets are a particular case of the interval type-2 fuzzy sets. For this reason, both concepts should be treated in a different way. In addition, the view presented in this paper will allow a more general perspective of interval type-2 fuzzy sets which will allow representing concepts which could not be presented by intervalvalued fuzzy sets.
Open Access
Co-occurrence of deep convolutional features for image search
(Elsevier, 2020) Forcén Carvalho, Juan Ignacio; Pagola Barrio, Miguel; Barrenechea Tartas, Edurne; Bustince Sola, Humberto; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Estadística, Informática y Matemáticas
Image search can be tackled using deep features from pre-trained Convolutional Neural Networks (CNN). The feature map from the last convolutional layer of a CNN encodes descriptive information from which a discriminative global descriptor can be obtained. We propose a new representation of co-occurrences from deep convolutional features to extract additional relevant information from this last convolutional layer. Combining this co-occurrence map with the feature map, we achieve an improved image representation. We present two different methods to get the co-occurrence representation, the first one based on direct aggregation of activations, and the second one, based on a trainable co-occurrence representation. The image descriptors derived from our methodology improve the performance in very well-known image retrieval datasets as we prove in the experiments.
Open Access
Quantifying repressive acts: explanation and challenges of the documentary archive of historical memory in Navarre
(2019) Majuelo Gil, Emilio; Mendiola Gonzalo, Fernando; Garmendia Amutxastegi, Gotzon; Piérola Narvarte, Gemma; García Funes, Juan Carlos; Yániz Berrio, Edurne; Pérez Ibarrola, Nerea; Barrenechea Tartas, Edurne; Rodríguez Martínez, Iosu; Sesma Redín, Rubén; Bustince Sola, Humberto; Ciencias Humanas y de la Educación; Giza eta Hezkuntza Zientziak; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
This document presents the historiographical and methodological foundations of the database of the Documentary Archive of Historical Memory in Navarre, which was developed in the Public University of Navarre following a commission from the Parliament and Government of Navarre. For this purpose a database was elaborated on the Francoist repression with the aim of including the great variety of repressive practices that historiography has identified. This database can be swiftly and easily consulted by the different social, institutional and academic agents. In the first place, the present document provides an assessment of the publication in several autonomous communities in recent years of different online databases on the victims of the civil war and the Francoist repression. Next, it introduces the unit of analysis of our database, “repressive acts”, which it inserts in the historiographical context of the Francoist repression and studies on violence. In the third section, a description is given of the different repressive categories and subcategories in which the repressive acts are framed. Finally, it presents some technical characteristics of the database’s internal organization and software.
Open Access
Extensions of fuzzy sets in image processing: an overview
(EUSFLAT, 2011) Pagola Barrio, Miguel; Barrenechea Tartas, Edurne; Bustince Sola, Humberto; Fernández Fernández, Francisco Javier; Galar Idoate, Mikel; Jurío Munárriz, Aránzazu; López Molina, Carlos; Paternain Dallo, Daniel; Sanz Delgado, José Antonio; Couto, P.; Melo Pinto, P.; Automática y Computación; Automatika eta Konputazioa
This work presents a valuable review for the interested reader of the recent Works using extensions of fuzzy sets in image processing. The chapter is divided as follows: first we recall the basics of the extensions of fuzzy sets, i.e. Type 2 fuzzy sets, interval-valued fuzzy sets and Atanassov’s intuitionistic fuzzy sets. In sequent sections we review the methods proposed for noise removal (sections 3), image enhancement (section 4), edge detection (section 5) and segmentation (section 6). There exist other image segmentation tasks such as video de-interlacing, stereo matching or object representation that are not described in this work.
Open Access
Initiative to increment the number of women in STEM degrees: women, science and technology chair of the Public University of Navarre
(IEEE, 2020) Aranguren Garacochea, Patricia; San Martín Biurrun, Idoia; Catalán Ros, Leyre; Martínez Ramírez, Alicia; Jurío Munárriz, Aránzazu; Díaz Lucas, Silvia; Pérez Artieda, Miren Gurutze; Gómez Fernández, Marisol; Barrenechea Tartas, Edurne; Estadística, Informática y Matemáticas; Ingeniería; Ingeniería Eléctrica, Electrónica y de Comunicación; Estatistika, Informatika eta Matematika; Ingeniaritza; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Gobierno de Navarra / Nafarroako Gobernua; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
The Public University of Navarre joined with Navarre Government has created the Women, Science and Technology Chair. This chair arises due to the plummeting tendency of the percentage of women in STEM degrees with the aim of reversing this trend. The programme of activities is defined throughout this contribution by six activities: a Theatre Play, a Poster Award on Final Degree/Masters Project, The 1st Week of Women, Science and Technology, the Promotion of Technical Degrees in schools and high-schools, a Workshop about Gender Stereotypes and the Fostering of Women among Science and Environment. Each activity gained great success and the preset goals were highly accomplished, especially, the 1st Week of Women, Science and Technology activity. The latter achieved a great success both in participation and in repercussion, contributing to visualize the role of women in science and technology.