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dc.contributor.authorRuiz de Miras, Juan
dc.contributor.authorLi, Yurong
dc.contributor.authorLeón, Alejandro
dc.contributor.authorArroyo, German
dc.contributor.authorLópez, Luis
dc.contributor.authorPomares Torres, Juan Carlos
dc.contributor.authorMartin, Domingo
dc.date.accessioned2026-07-01T07:48:11Z
dc.date.available2026-07-01T07:48:11Z
dc.date.issued2025
dc.identifier.citationRuiz de Miras, J., Li, Y., León, A., Arroyo, G., López, L., Pomares Torres, J. C., y Martin, D. (2025). Ultra-fast computation of fractal dimension for RGB images. PATTERN ANALYSIS AND APPLICATIONS, 28(1), 36. https://doi.org/10.1007/s10044-025-01415-yes
dc.identifier.issn1433-7541, 1433-755X
dc.identifier.urihttp://hdl.handle.net/20.500.12251/4257
dc.description.abstractThe fractal dimension (FD) is a quantitative parameter widely used to analyze digital images in many application fields such as image segmentation, feature extraction, object recognition, texture analysis, and image compression and denoising, among many others. A variety of algorithms have been previously proposed for estimating the FD, however most of them are limited to binary or gray-scale images only. In recent years, several authors have proposed algorithms for computing the FD of color images. Nevertheless, almost all these methods are computationally inefficient when analyzing large images. Nowadays, color images can be very large in size, and there is a growing trend toward even larger datasets. This implies that the time required to calculate the FD of such datasets can become extremely long. In this paper we present a very efficient GPU algorithm, implemented in CUDA, for computing the FD of RGB color images. Our solution is an extension to RGB of the differential box-counting (DBC) algorithm for gray-scale images. Our implementation simplifies the box-counting computation to very simple operations which are easily combined across iterations. We evaluated our algorithm on two distinct hardware/software platforms using a set of images of increasing size. The performance of our method was compared against two recent FD algorithms for RGB images: a fast box-merging GPU algorithm, and the most advanced approach based on extending the DBC method. The results showed that our GPU algorithm performed very well and achieved speedups of up to 7.9x and 6172.6x regarding these algorithms, respectively. In addition, our algorithm achieved average error rates similar to those obtained by the two reference algorithms when estimating the FD for synthetic images with known FD values, and even outperformed them when processing large images. These results suggest that our GPU algorithm offers a highly reliable and ultra-fast solution for estimating the FD of color images.es
dc.language.isoenges
dc.publisherSpringeres
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleUltra-fast computation of fractal dimension for RGB imageses
dc.typearticle
dc.identifier.doi10.1007/s10044-025-01415-y
dc.issue.number1es
dc.journal.titlePATTERN ANALYSIS AND APPLICATIONSes
dc.rights.accessRightsopenAccesses
dc.subject.keywordAlgoritmoses
dc.subject.keywordVentilación (Construcción)es
dc.subject.keywordVentanases
dc.subject.keywordCambio climáticoes
dc.subject.keywordEmisiones de CO2es
dc.subject.keywordAislamiento térmicoes
dc.subject.keywordSimulación energética - herramientases
dc.subject.unesco1203.17 Informáticaes
dc.subject.unesco3313.01 Ventiladoreses
dc.subject.unesco3322 Tecnología Energéticaes
dc.subject.unesco3308 Ingeniería y Tecnología del Medio Ambientees
dc.subject.unesco3305.14 Viviendases
dc.volume.number28
dc.item.number36es


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional