Person:
Flores Vidal, Pablo Arcadio

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First Name
Pablo Arcadio
Last Name
Flores Vidal
URI
https://hdl.handle.net/20.500.14352/80733
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Estudios estadísticos
Department
Estadística y Ciencia de los Datos
Area
Estadística e Investigación Operativa
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Author: Castro Cantalejo, Javier ×

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    A new approach to Color Edge Detection
    (Proceedings of the 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019), 2019) Flores Vidal, Pablo Arcadio; Gómez González, Daniel; Castro Cantalejo, Javier; Villarino, Guillermo; Montero, Javier
    Most edge detection algorithms deal only with grayscale images, and the way of adapting them to use with RGB images is an open problem. In this work, we explore different ways of aggregating the color information of a RGB image for edges extraction, and this is made by means of well-known edge detection algorithms. In this research, it is been used the set of images from Berkeley. In order to evaluate the algorithm’s performance, F measure is computed. The way that color information -the different channels- is aggregated is proved to be relevant for the edge detection task. Moreover, post-aggregation of channels performed significatively better than the classic approach (pre-aggregation of channels).
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    New Aggregation Approaches with HSV to Color Edge Detection
    (International Journal of Computational Intelligence Systems, 2022) Flores Vidal, Pablo Arcadio; Gómez González, Daniel; Castro Cantalejo, Javier; Montero, Javier
    The majority of edge detection algorithms only deal with grayscale images, while their use with color images remains an open problem. This paper explores different approaches to aggregate color information of RGB and HSV images for edge extraction purposes through the usage of the Sobel operator and Canny algorithm. This paper makes use of Berkeley’s image data set, and to evaluate the performance of the different aggregations, the F-measure is computed. Higher potential of aggregations with HSV channels than with RGB channels is found. This article also shows that depending on the type of image used, RGB or HSV, some methods are more appropriate than others.