000109469 001__ 109469
000109469 005__ 20230519145512.0
000109469 0247_ $$2doi$$a10.3390/e23101261
000109469 0248_ $$2sideral$$a125672
000109469 037__ $$aART-2021-125672
000109469 041__ $$aeng
000109469 100__ $$aEspinosa, Ricardo
000109469 245__ $$aTwo-Dimensional EspEn: A New Approach to Analyze Image Texture by Irregularity
000109469 260__ $$c2021
000109469 5060_ $$aAccess copy available to the general public$$fUnrestricted
000109469 5203_ $$aImage processing has played a relevant role in various industries, where the main challenge is to extract specific features from images. Specifically, texture characterizes the phenomenon of the occurrence of a pattern along the spatial distribution, taking into account the intensities of the pixels for which it has been applied in classification and segmentation tasks. Therefore, several feature extraction methods have been proposed in recent decades, but few of them rely on entropy, which is a measure of uncertainty. Moreover, entropy algorithms have been little explored in bidimensional data. Nevertheless, there is a growing interest in developing algorithms to solve current limits, since Shannon Entropy does not consider spatial information, and SampEn2D generates unreliable values in small sizes. We introduce a proposed algorithm, EspEn (Espinosa Entropy), to measure the irregularity present in two-dimensional data, where the calculation requires setting the parameters as follows: m (length of square window), r (tolerance threshold), and ρ (percentage of similarity). Three experiments were performed; the first two were on simulated images contaminated with different noise levels. The last experiment was with grayscale images from the Normalized Brodatz Texture database (NBT). First, we compared the performance of EspEn against the entropy of Shannon and SampEn2D. Second, we evaluated the dependence of EspEn on variations of the values of the parameters m, r, and ρ. Third, we evaluated the EspEn algorithm on NBT images. The results revealed that EspEn could discriminate images with different size and degrees of noise. Finally, EspEn provides an alternative algorithm to quantify the irregularity in 2D data; the recommended parameters for better performance are m = 3, r = 20, and ρ = 0.7.
000109469 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000109469 590__ $$a2.738$$b2021
000109469 592__ $$a0.553$$b2021
000109469 594__ $$a4.4$$b2021
000109469 591__ $$aPHYSICS, MULTIDISCIPLINARY$$b42 / 86 = 0.488$$c2021$$dQ2$$eT2
000109469 593__ $$aElectrical and Electronic Engineering$$c2021$$dQ2
000109469 593__ $$aPhysics and Astronomy (miscellaneous)$$c2021$$dQ2
000109469 593__ $$aInformation Systems$$c2021$$dQ2
000109469 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000109469 700__ $$0(orcid)0000-0003-1272-0550$$aBailón, Raquel$$uUniversidad de Zaragoza
000109469 700__ $$0(orcid)0000-0003-3434-9254$$aLaguna, Pablo$$uUniversidad de Zaragoza
000109469 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000109469 773__ $$g23, 10 (2021), e23101261$$pEntropy$$tENTROPY$$x1099-4300
000109469 8564_ $$s4171464$$uhttps://zaguan.unizar.es/record/109469/files/texto_completo.pdf$$yVersión publicada
000109469 8564_ $$s2674705$$uhttps://zaguan.unizar.es/record/109469/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000109469 909CO $$ooai:zaguan.unizar.es:109469$$particulos$$pdriver
000109469 951__ $$a2023-05-18-15:12:47
000109469 980__ $$aARTICLE