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. 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. In the amplitude and intensity formats, this noise is multiplicative and non-Gaussian requiring, thus, specialized techniques for image processing and understanding. 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. The former is computed by a measure of entropy, while the latter depends on the definition of a stochastic divergence. It is a functional that captures the notions of order/disorder and of distance to an equilibrium distribution. We show that the proposed filter also enhances the polarimetric entropy and preserves the scattering information of the targets.Ībstract.A new generalized Statistical Complexity Measure (SCM) was proposed by Rosso et al in 2010. Image quality assessment methods on simulated and real data are employed to validate the performance of this approach. This novel technique was compared with the Boxcar, Refined Lee and IDAN filters. The test stems from the family of (h-φ) divergences which originated in Information Theory. 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. This proposal uses the complex Wishart model to describe PolSAR data, but the technique can be extended to other models. The main objective is to select homogeneous pixels in the filtering area through statistical tests between distributions. 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. Users should refer to the original published version of the material for the full abstract.Abstract. No warranty is given about the accuracy of the copy. However, users may print, download, or email articles for individual use. (English) Copyright of Colloquium Exactarum is the property of Asociacao Prudentina de Educacao e Cultura (APEC) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. The modeling uses Linear Algebra concepts: matrices, determinants and linear systems, together with the concept of the set of the rest of the division by 2(Z2). The game, Lights Out, is a famous game of the 90's, which consists of 25 keys illuminated and arranged in the form of a 5x5 matrix, where it has an initial state and we must delete all keys by pressing a correct sequence of keys, this sequence will be provided by the program implemented in Scilab, ie we will have an immediate solution to erase all lights in the game. Abstract: Linear Algebra presents a very important role in the areas of accuracy, and through it we can show its utility in modeling a problem that involves a simple game of erasing and lighting lights, after modeling the problem, we will solve it by implementing an algorithm of elimination of Gauss in Scilab (Scilab is free and open source software, focused on numerical computation similar to Matlab).
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