How good are MatLab, Octave and Scilab for computational modelling?

Almeida, Eliana Silva de; Medeiros, Antonio C.; Frery, Alejandro C.

doi:10.1590/s1807-03022012000300005

Cited by 9 publications

(9 citation statements)

References 14 publications

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“…The time required to filter a 128 × 128 pixels image with one iteration is of about 75 s in an Intel R Core TM i7-3632QM CPU 2.20 GHz, with software developed in R version 2.14.1 running on Ubuntu 12.04. R was the choice because of its excellent accuracy with respect to similar platforms [1].…”

Section: A Computational Informationmentioning

confidence: 99%

Speckle reduction in polarimetric SAR imagery with stochastic distances and nonlocal means

Torres

Sant’Anna

Freitas

et al. 2014

Pattern Recognition

131

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Abstract. This paper presents a technique for reducing speckle in Polarimetric Synthetic Aperture Radar (PolSAR) imagery using Nonlocal Means and a statistical test based on stochastic divergences. The main objective is to select homogeneous pixels in the filtering area through statistical tests between distributions. This proposal uses the complex Wishart model to describe PolSAR data, but the technique can be extended to other models. The weights of the location-variant linear filter are function of the p-values of tests which verify the hypothesis that two samples come from the same distribution and, therefore, can be used to compute a local mean. The test stems from the family of (h-φ) divergences which originated in Information Theory. This novel technique was compared with the Boxcar, Refined Lee and IDAN filters. Image quality assessment methods on simulated and real data are employed to validate the performance of this approach. We show that the proposed filter also enhances the polarimetric entropy and preserves the scattering information of the targets.

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Section: A Computational Informationmentioning

confidence: 99%

Speckle reduction in polarimetric SAR imagery with stochastic distances and nonlocal means

Torres

Sant’Anna

Freitas

et al. 2014

Pattern Recognition

131

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“…This platform is freely available at http://www.r-project.org for a diversity of computational platforms, and its excellent numerical properties have been attested in [2,3].…”

Section: Resultsmentioning

confidence: 99%

Generalized Statistical Complexity of SAR Imagery

Almeida

Medeiros

Rosso

et al. 2012

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract.A new generalized Statistical Complexity Measure (SCM) was proposed by Rosso et al in 2010. It is a functional that captures the notions of order/disorder and of distance to an equilibrium distribution. The former is computed by a measure of entropy, while the latter depends on the definition of a stochastic divergence. When the scene is illuminated by coherent radiation, image data is corrupted by speckle noise, as is the case of ultrasound-B, sonar, laser and Synthetic Aperture Radar (SAR) sensors. In the amplitude and intensity formats, this noise is multiplicative and non-Gaussian requiring, thus, specialized techniques for image processing and understanding. One of the most successful family of models for describing these images is the Multiplicative Model which leads, among other probability distributions, to the G 0 law. This distribution has been validated in the literature as an expressive and tractable model, deserving the "universal" denomination for its ability to describe most types of targets. In order to compute the statistical complexity of a site in an image corrupted by speckle noise, we assume that the equilibrium distribution is that of fully developed speckle, namely the Gamma law in intensity format, which appears in areas with little or no texture. We use the Shannon entropy along with the Hellinger distance to measure the statistical complexity of intensity SAR images, and we show that it is an expressive feature capable of identifying many types of targets.

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“…MatLab(1)/Fortran Octave(1)/MatLab(1) 2 6 × 2 6 -30,800 2 7 × 2 7 7,5000 80,400 2 8 × 2 8 8,1250 75,015 2 9 × 2 9 11,591 86,863 2 10 × 2 10 18,214 73,522 2 11 × 2 11 23,537 49,959 2 12 × 2 12 36,917 33,316 2 13 × 2 13 58,761 21,712…”

Section: Ordemmentioning

confidence: 99%

“…Desse modo, pesquisadores vêm explorando as potencialidades das duas linguagens na computação científica, como por exemplo, em cálculo numérico [4], operações elementares com funções e matrizes [7], métodos computacionais em álgebra linear [8], métodos numéricos em equações diferenciais [9], entre outros.…”

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“…Comparações entre os pacotes do MatLab e os pacotes de softwares livres Octave, FreeMat e Scilab foram realizadas com a proposta de encontrar uma alternativa de software livre para o MatLab, em alguns ambientes educacionais e de pesquisas [10]. Existem ainda comparações voltadas para a ordem de precisão que os pacotes conseguem atingir [8], dependendo do sistema operacional utilizado, e comparações entre as escritas das linguagens.…”

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Métodos numéricos aplicados às equações de Euler: comparação entre MatLab, Octave e Fortran

Garcia¹,

Silveira²

2017

C.Q.D.

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Increasingly computational resources are required in mathematical modeling, then interpreted programming languages gain more space in scientific computing and applied mathematics.The purpose of this study was to evaluate the structures of the languages interpreted MatLab and Octave, important in the writing of codes to obtain approximate solutions of EDP's. Euler's equations that describing the flow of ideal fluids were used and their solutions were found via finite volume numerical methods: Lax-Friedrich, Nessyahu-Tadmor, Lax-Wendroff and Godunov. In all situations, we compared MatLab and Octave with the compiled language Fortran 90. The results showed that execution time and computational cost depend not only on the method chosen to solve a given mathematical problem, but also on the programming language chosen to implement the methods, coupled with the programming technique used.

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How good are MatLab, Octave and Scilab for computational modelling?

Cited by 9 publications

References 14 publications

Speckle reduction in polarimetric SAR imagery with stochastic distances and nonlocal means

Speckle reduction in polarimetric SAR imagery with stochastic distances and nonlocal means

Generalized Statistical Complexity of SAR Imagery

Métodos numéricos aplicados às equações de Euler: comparação entre MatLab, Octave e Fortran

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