Person:
López Molina, Carlos

Profile Picture

Email Address

person.page.identifierURI

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

Birth Date

Research Projects

Organizational Units

Job Title

Last Name

López Molina

First Name

Carlos

person.page.departamento

Estadística, Informática y Matemáticas

person.page.instituteName

ORCID

0000-0002-0904-9834

person.page.upna

810097

Name

Filters

2011 - 20182
Reset filters

Settings

Author: Fernández Fernández, Francisco Javier × Start date: 2011 × End date: 2019 ×

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    PublicationOpen 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.
  • Thumbnail Image
    PublicationOpen 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.