Open Access
Evolution in time of L-fuzzy context sequences
(Elsevier, 2016) Alcalde, Cristina; Burusco Juandeaburre, Ana; Bustince Sola, Humberto; Jurío Munárriz, Aránzazu; Sanz Delgado, José Antonio; Automatika eta Konputazioa; Institute of Smart Cities - ISC; Automática y Computación; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
In this work, we consider a complete lattice L and we study L-fuzzy context sequences which represent the evolution in time of an L-fuzzy context. To carry out this study, in the first part of the paper, we consider n-ary OWA operators in complete lattices, which enable us to make a general analysis and a temporal analysis at any moment in time of L-fuzzy context sequences. After that, evolution in time of the relationship between the objects and the attributes is considered. In particular, we analyze the concepts of Trend and Persistent formal contexts. Finally, we illustrate our results with an example where we consider the particular lattice L = J ([0, 1]).
Open Access
Linking mathematical morphology and L-fuzzy concepts
(World Scientific, 2017) Alcalde, Cristina; Burusco Juandeaburre, Ana; Bustince Sola, Humberto; Fuentes González, Ramón; Sesma Sara, Mikel; Automatika eta Konputazioa; Institute of Smart Cities - ISC; Automática y Computación
In this paper we study the relation between L-fuzzy morphology and L-fuzzy concepts over complete lattices. In particular, we show how the erosion and dilation operators of the former can be understood in terms of the derivation operators of the latter, even when the set of objects is different from the set of attributes.
Open Access
New measures for comparing matrices and their application to image processing
(Elsevier, 2018) Sesma Sara, Mikel; Miguel Turullols, Laura de; Pagola Barrio, Miguel; Burusco Juandeaburre, Ana; Mesiar, Radko; Bustince Sola, Humberto; Automatika eta Konputazioa; Institute of Smart Cities - ISC; Automática y Computación; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
In this work we present the class of matrix resemblance functions, i.e., functions that measure the difference between two matrices. We present two construction methods and study the properties that matrix resemblance functions satisfy, which suggest that this class of functions is an appropriate tool for comparing images. Hence, we present a comparison method for grayscale images whose result is a new image, which enables to locate the areas where both images are equally similar or dissimilar. Additionally, we propose some applications in which this comparison method can be used, such as defect detection in industrial manufacturing processes and video motion detection and object tracking.
Open Access
Trend analysis in L-fuzzy contexts with absent values
(University of Sistan and Baluchestan (Irán), 2020) Alcalde, Cristina; Burusco Juandeaburre, Ana; Bustince Sola, Humberto; Sesma Sara, Mikel; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Sometimes we have to work with L-fuzzy context sequences where one or more values are missing. These sequences can represent, among other things, the evolution in time of an L-fuzzy context. The studies of tendencies that we have done so far used tools that are not valid when the L-fuzzy context has unknown values. In this work we address such situations and we propose new methods to tackle the problem. Besides, we use the study of tendencies to analyse relations between the objects and the attributes of L-fuzzy contexts and to replace the absent values taking into account the behaviour of the sequence.

Burusco Juandeaburre, Ana

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