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López Molina, Carlos

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López Molina

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Carlos

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Estadística, Informática y Matemáticas

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0000-0002-0904-9834

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810097

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Now showing 1 - 6 of 6
  • PublicationOpen 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.
  • PublicationOpen Access
    Twofold binary image consensus for medical imaging meta-analysis
    (Springer, 2018) López Molina, Carlos; Sánchez Ruiz de Gordoa, Javier; Zelaya Huerta, María Victoria; Baets, Bernard de; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
    In the field of medical imaging, ground truth is often gathered from groups of experts, whose outputs are generally heterogeneous. This procedure raises questions on how to compare the results obtained by automatic algorithms to multiple ground truth items. Secondarily, it raises questions on the meaning of the divergences between experts. In this work, we focus on the case of immunohistochemistry image segmentation and analysis. We propose measures to quantify the divergence in groups of ground truth images, and we observe their behaviour. These measures are based upon fusion techniques for binary images, which is a common example of non-monotone data fusion process. Our measures can be used not only in this specific field of medical imagery, but also in any task related to meta-quality evaluation for image processing, e.g. ground truth validation or expert rating.
  • PublicationOpen Access
    Distance transformations based on ordered weighted averaging operators
    (University of Hawaii Press, 2021) López Molina, Carlos; Miguel Turullols, Laura de; Iglesias Rey, Sara; Bustince Sola, Humberto; Baets, Bernard de; Estadística, Informática y Matemáticas; 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.
  • PublicationOpen 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.
  • PublicationOpen 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.
  • PublicationOpen 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.