Person:
López Molina, Carlos

person.page.identifierURI

https://academica-e.unavarra.es/handle/2454/49448

Last Name

López Molina

First Name

Carlos

person.page.departamento

Estadística, Informática y Matemáticas

ORCID

0000-0002-0904-9834

person.page.upna

810097

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Now showing 1 - 10 of 27

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
A survey of fingerprint classification Part I: taxonomies on feature extraction methods and learning models
(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
This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models considered for singular point detection and for orientation map extraction. Then, we focus on the different learning models considered to build the classifiers used to label new fingerprints. Taxonomies and classifications for the feature extraction, singular point detection, orientation extraction and learning methods are presented. A critical view of the existing literature have led us to present a discussion on the existing methods and their drawbacks such as difficulty in their reimplementation, lack of details or major differences in their evaluations procedures. On this account, an experimental analysis of the most relevant methods is carried out in the second part of this paper, and a new method based on their combination is presented.
Restricted
Servicios de localización para terminales moviles en redes WiFi
(2006) López Molina, Carlos; Escuela Técnica Superior de Ingenieros Industriales y de Telecomunicación; Telekomunikazio eta Industria Ingeniarien Goi Mailako Eskola Teknikoa
Open Access
From restricted equivalence functions on Ln to similarity measures between fuzzy multisets
(IEEE, 2023) Ferrero Jaurrieta, Mikel; Takáč, Zdenko; Rodríguez Martínez, Iosu; Marco Detchart, Cedric; Bernardini, Ángela; Fernández Fernández, Francisco Javier; López Molina, Carlos; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Restricted equivalence functions are well-known functions to compare two numbers in the interval between 0 and 1. Despite the numerous works studying the properties of restricted equivalence functions and their multiple applications as support for different similarity measures, an extension of these functions to an n-dimensional space is absent from the literature. In this paper, we present a novel contribution to the restricted equivalence function theory, allowing to compare multivalued elements. Specifically, we extend the notion of restricted equivalence functions from L to L n and present a new similarity construction on L n . Our proposal is tested in the context of color image anisotropic diffusion as an example of one of its many applications.
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
Ultrametrics for context-aware comparison of binary images
(Elsevier, 2024) López Molina, Carlos; Iglesias Rey, Sara; Baets, Bernard de; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Quantitative image comparison has been a key topic in the image processing literature for the past 30 years. The reasons for it are diverse, and so is the range of applications in which measures of comparison are needed. Examples of image processing tasks requiring such measures are the evaluation of algorithmic results (through the comparison of computer-generated results to given ground truth) or the selection of loss/goal functions in a machine learning context. Measures of comparison in literature take different inspirations, and are often tailored to specific needs. Nevertheless, even if some measures of comparison intend to replicate how humans evaluate the similarity of two images, they normally overlook a fundamental characteristic of the way humans perform such evaluation: the context of comparison. In this paper, we present a measure of comparison for binary images that incorporates a sense of context. More specifically, we present a Methodology for the generation of ultrametrics for context-aware comparison of binary images. We test our proposal in the context of boundary image comparison on the BSDS500 benchmark.
Open Access
Content-aware image smoothing based on fuzzy clustering
(Springer, 2022) Antunes dos Santos, Felipe; López Molina, Carlos; Mir Fuentes, Arnau; Mendióroz Iriarte, Maite; Baets, Bernard de; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Literature contains a large variety of content-aware smoothing methods. As opposed to classical smoothing methods, content-aware ones intend to regularize the image while avoiding the loss of relevant visual information. In this work, we propose a novel approach to contentaware image smoothing based on fuzzy clustering, specifically the Spatial Fuzzy c-Means (SFCM) algorithm. We develop the proposal and put it to the test in the context of automatic analysis of immunohistochemistry imagery for neural tissue analysis.
Open Access
On the role of distance transformations in Baddeley's Delta Metric
(Elsevier, 2021) López Molina, Carlos; Iglesias Rey, Sara; Bustince Sola, Humberto; Baets, Bernard de; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Comparison and similarity measurement have been a key topic in computer vision for a long time. There is, indeed, an extensive list of algorithms and measures for image or subimage comparison. The superiority or inferiority of different measures is hard to scrutinize, especially considering the dimensionality of their parameter space and their many different configurations. In this work, we focus on the comparison of binary images, and study different variations of Baddeley's Delta Metric, a popular metric for such images. We study the possible parameterizations of the metric, stressing the numerical and behavioural impact of different settings. Specifically, we consider the parameter settings proposed by the original author, as well as the substitution of distance transformations by regularized distance transformations, as recently presented by Brunet and Sills. We take a qualitative perspective on the effects of the settings, and also perform quantitative experiments on separability of datasets for boundary evaluation.
Open Access
Multiscale edge detection using first-order derivative of anisotropic Gaussian kernels
(Springer, 2019) Wang, Gang; López Molina, Carlos; Baets, Bernard de; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Spatially scaled edges are ubiquitous in natural images. To better detect edges with heterogeneous widths, in this paper, we propose a multiscale edge detection method based on first-order derivative of anisotropic Gaussian kernels. These kernels are normalized in scale-space, yielding a maximum response at the scale of the observed edge, and accordingly, the edge scale can be identified. Subsequently, the maximum response and the identified edge scale are used to compute the edge strength. Furthermore, we propose an adaptive anisotropy factor of which the value decreases as the kernel scale increases. This factor improves the noise robustness of small-scale kernels while alleviating the anisotropy stretch effect that occurs in conventional anisotropic methods. Finally, we evaluate our method on widely used datasets. Experimental results validate the benefits of our method over the competing methods.
Open Access
Image feature extraction using OD-monotone functions
(Springer, 2018) Marco Detchart, Cedric; López Molina, Carlos; Fernández Fernández, Francisco Javier; Pagola Barrio, Miguel; Bustince Sola, Humberto; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Estadística, Informática y Matemáticas
Edge detection is a basic technique used as a preliminary step for, e.g., object extraction and recognition in image processing. Many of the methods for edge detection can be fit in the breakdown structure by Bezdek, in which one of the key parts is feature extraction. This work presents a method to extract edge features from a grayscale image using the so-called ordered directionally monotone functions. For this purpose we introduce some concepts about directional monotonicity and present two construction methods for feature extraction operators. The proposed technique is competitive with the existing methods in the literature. Furthermore, if we combine the features obtained by different methods using penalty functions, the results are equal or better results than stateof-the-art methods.