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Iglesias Rey, Sara

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https://academica-e.unavarra.es/handle/2454/49271

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Iglesias Rey

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Sara

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Estadƭstica, InformƔtica y MatemƔticas

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0000-0002-7874-4443

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811936

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  • Thumbnail Image
    PublicationOpen Access
    On the role of distance transformations in Baddeley's Delta Metric
    (Elsevier, 2021) LĆ³pez Molina, Carlos; Iglesias Rey, Sara; Bustince Sola, Humberto; Baets, Bernard de; EstadĆ­stica, InformĆ”tica y MatemĆ”ticas; 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.
  • Thumbnail Image
    PublicationOpen Access
    VisualizaciĆ³n de imĆ”genes multi-e hiperespectrales usando operadores de agregaciĆ³n
    (2018) Iglesias Rey, Sara; LĆ³pez Molina, Carlos; Escuela TĆ©cnica Superior de Ingenieros AgrĆ³nomos; Nekazaritza Ingeniarien Goi Mailako Eskola Teknikoa
    El crecimiento de las imĆ”genes multi- e hiperespectrales ha sido claro en los Ćŗltimos aƱos, y nada hace pensar que su uso decaerĆ” a corto plazo. Sin embargo, sufre un problema debido a la alta dimensionalidad de la informaciĆ³n almacenada en cada pĆ­xel: los problemas para su visualizaciĆ³n en monitores estĆ”ndar (tri-estĆ­mulo). Por ello, en el presente trabajo se ha propuesto un mĆ©todo de fusiĆ³n de los datos mediante operadores de agregaciĆ³n, empleando para ello los operadores OWA (Ordered Weighted Averaging) para obtener una representaciĆ³n inicial de la informaciĆ³n. El comportamiento del proceso propuesto se evaluĆ³ y comparĆ³ con el mĆ©todo clĆ”sico de fusiĆ³n de la informaciĆ³n. En particular, se han estudiado distintos vectores de pesos wi, mostrando la gran influencia que estos tienen sobre la visualizaciĆ³n. Finalmente, se realizĆ³ una aplicaciĆ³n de los operadores de agregaciĆ³n escogidos a una serie de casos prĆ”cticos de conjuntos de datos de imĆ”genes multi- e hiperespectrales obtenidos de las Universidad de PaĆ­s Vasco. Se ha comprobado la utilidad y mejora de este mĆ©todo de visualizaciĆ³n, respecto a otras propuestas anteriores, cumpliendo los objetivos de diseƱo propuestos presentados a lo largo de este estudio.
  • Thumbnail Image
    PublicationOpen Access
    Ultrametrics for context-aware comparison of binary images
    (Elsevier, 2024) LĆ³pez Molina, Carlos; Iglesias Rey, Sara; Baets, Bernard de; EstadĆ­stica, InformĆ”tica y MatemĆ”ticas; 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.
  • Thumbnail Image
    PublicationOpen Access
    Distance transformations based on ordered weighted averaging operators
    (University of Hawaii Press, 2021) LĆ³pez Molina, Carlos; Miguel Turullols, Laura de; Iglesias Rey, Sara; Bustince Sola, Humberto; Baets, Bernard de; EstadĆ­stica, InformĆ”tica y MatemĆ”ticas; Estatistika, Informatika eta Matematika
    Binary image comparison has been a study subject for a long time, often rendering in context-specific solutions that depend upon the type of visual contents in the binary images. Distance transformations have been a recurrent tool in many of such solutions. The literature contains works on the generation and definition of distance transformations, but also on how to make a sensible use of their results. In this work, we attempt to solve one of the most critical problems in the application of distance transformations to real problems: their oversensitivity to certain spurious pixels which, even if having a minimal visual impact in the binary images to be compared, may have a severe impact on their distance transforms. With this aim, we combine distance transformations with Ordered Weighted Averaging (OWA) operators, a well-known information fusion tool from Fuzzy Set Theory.