New measures for comparing matrices and their application to image processing
Fecha
2018Autor
Versión
Acceso abierto / Sarbide irekia
Tipo
Artículo / Artikulua
Versión
Versión aceptada / Onetsi den bertsioa
Identificador del proyecto
ES/1PE/TIN2016-77356
Impacto
|
10.1016/j.apm.2018.05.006
Resumen
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
re ...
[++]
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. [--]
Materias
Matrix resemblance functions,
Restricted equivalence functions,
Inclusion grades,
Fuzzy mathematical morphology,
Defect detection,
Motion detection
Editor
Elsevier
Publicado en
Applied mathematical modelling, vol. 61, september 2018, pp. 498-520
Departamento
Universidad Pública de Navarra. Departamento de Automática y Computación /
Nafarroako Unibertsitate Publikoa. Automatika eta Konputazioa Saila /
Universidad Pública de Navarra/Nafarroako Unibertsitate Publikoa. Institute of Smart Cities - ISC
Versión del editor
Entidades Financiadoras
This work is partially supported by the research services of
Universidad Publica de Navarra
and by the project TIN2016-77356-P (AEI/FEDER, UE). R. Mesiar is supported by Slovak grant
APVV-14-0013, and by Czech Project LQ1602 “IT4Innovations excellence in science”.