Search results for "pattern"

showing 10 items of 4203 documents

Subsignal-based denoising from piecewise linear or constant signal

2011

15 pages; International audience; n the present work, a novel signal denoising technique for piecewise constant or linear signals is presented termed as "signal split." The proposed method separates the sharp edges or transitions from the noise elements by splitting the signal into different parts. Unlike many noise removal techniques, the method works only in the nonorthogonal domain. The new method utilizes Stein unbiased risk estimate (SURE) to split the signal, Lipschitz exponents to identify noise elements, and a polynomial fitting approach for the sub signal reconstruction. At the final stage, merging of all parts yield in the fully denoised signal at a very low computational cost. St…

Mathematical optimization[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingComputer scienceStochastic resonanceNoise reduction[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technology01 natural sciencesMultiplicative noisePiecewise linear function010104 statistics & probabilitySpeckle patternsymbols.namesakeSignal-to-noise ratioWavelet[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing0202 electrical engineering electronic engineering information engineering0101 mathematicsSignal transfer functionShrinkageSignal reconstructionNoise (signal processing)General EngineeringNonlinear opticsWavelet transform020206 networking & telecommunicationsTotal variation denoisingAtomic and Molecular Physics and OpticsAdditive white Gaussian noiseGaussian noisePiecewisesymbolsStep detectionAlgorithm[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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The squared symmetric FastICA estimator

2017

In this paper we study the theoretical properties of the deflation-based FastICA method, the original symmetric FastICA method, and a modified symmetric FastICA method, here called the squared symmetric FastICA. This modification is obtained by replacing the absolute values in the FastICA objective function by their squares. In the deflation-based case this replacement has no effect on the estimate since the maximization problem stays the same. However, in the symmetric case we obtain a different estimate which has been mentioned in the literature, but its theoretical properties have not been studied at all. In the paper we review the classic deflation-based and symmetric FastICA approaches…

Mathematical optimizationaffine equivarianceminimum distance indexMathematics - Statistics TheoryIndependent component analysis02 engineering and technologyEstimating equationsStatistics Theory (math.ST)01 natural sciences010104 statistics & probabilityMatrix (mathematics)0202 electrical engineering electronic engineering information engineeringFOS: MathematicsApplied mathematics62H10 62H120101 mathematicsElectrical and Electronic EngineeringMathematicsta113ta112ta111EstimatorContrast (statistics)riippumattomien komponenttien analyysi020206 networking & telecommunicationsMaximizationIndependent component analysisNonlinear systemControl and Systems EngineeringSignal ProcessingFastICAComputer Vision and Pattern Recognitionlimiting normalitySoftware
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Patch-Based Image Denoising Model for Mixed Gaussian Impulse Noise Using L1 Norm

2017

Image denoising is the classes of technique used to free the image form the noise. The noise in the image may be added during the observation process due to the improper setting of the camera lance, low-resolution camera, cheap, and low-quality sensors, etc. Noise in the image may also be added during the image restoration, image transmission through the transmission media. To obtain required information from image, image must be noise free, i.e., high-frequency details must be present in the image. There are number of applications where image denoising is needed such as remote location detection, computer vision, computer graphics, video surveillance, etc. In last two decades, numbers of m…

Mathematical optimizationbusiness.industryComputer scienceGaussianComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONTransmission mediumImpulse (physics)Non-local meansImpulse noiseComputer graphicssymbols.namesakeGaussian noiseComputer Science::Computer Vision and Pattern RecognitionsymbolsComputer visionArtificial intelligencebusinessImage restoration
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Representation and estimation of spectral reflectances using projection on PCA and wavelet bases

2008

In this article, we deal with the problem of spectral reflectance function representation and estimation in the context of multispectral imaging. Because the reconstruction of such functions is an inverse problem, slight variations in input data completely skew the expected results. Therefore, stabilizing the reconstruction process is necessary. To do this, we propose to use wavelets as basis functions, and we compare those with Fourier and PCA bases. We present the idea and compare these three methods, which belong to the class of linear models. The PCA method is training-set dependent and confirms its robustness when applied to reflectance estimation of the training sets. Fourier and wave…

Mathematical optimizationbusiness.industryGeneral Chemical EngineeringMultispectral imageHuman Factors and ErgonomicsBasis functionPattern recognitionGeneral ChemistryInverse problemsymbols.namesakeWaveletFourier transformRobustness (computer science)Principal component analysissymbolsFunction representationArtificial intelligencebusinessMathematicsColor Research & Application
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Preface

2015

This special issue of Mathematical Structures in Computer Science is devoted to the fourteenth Italian Conference on Theoretical Computer Science (ICTCS) held at University of Palermo, Italy, from 9th to 11th September 2013. ICTCS is the conference of the Italian Chapter of the European Association for Theoretical Computer Science and covers a wide spectrum of topics in Theoretical Computer Science, ranging from computational complexity to logic, from algorithms and data structure to programming languages, from combinatorics on words to distributed computing. For this reason, the contributions here included come from very different areas of Theoretical Computer Science. In fact this special…

Mathematics (miscellaneous)Computer Science Applications1707 Computer Vision and Pattern RecognitionComputer Science Applications
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Projector operators in clustering

2016

In a recent paper the notion of {\em quantum perceptron} has been introduced in connection with projection operators. Here we extend this idea, using these kind of operators to produce a {\em clustering machine}, i.e. a framework which generates different clusters from a set of input data. Also, we consider what happens when the orthonormal bases first used in the definition of the projectors are replaced by frames, and how these can be useful when trying to connect some noised signal to a given cluster.

Mathematics - Functional AnalysisEngineering (all)FOS: MathematicsCluster analysis harmonic analysis on Euclidean spaces pattern recognitionMathematics (all)Settore MAT/07 - Fisica MatematicaFunctional Analysis (math.FA)
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Some Remarks on Calabi-Yau Manifolds

2010

Here we focus on the geometry of the “mirror quintic” Y and its generalizations. In particular, we illustrate how to obtain new birational models of Y . The article under review can be regarded as an announcement of or supplement to results in forthcoming papers of the author and his collaborators concerning quintic threefolds, the Dwork pencil, and its natural generalization to higher dimensions [G. Bini, “Quotients of hypersurfaces in weighted projective space”, preprint, arxiv.org/ abs/0905.2099, Adv. Geom., to appear; G. Bini, B. van Geemen and T. L. Kelly, “Mirror quintics, discrete symmetries and Shioda maps”, preprint, arxiv.org/abs/0809. 1791, J. Algebraic Geom., to appear; G. Bini …

Mathematics::Algebraic GeometryQA1-939calabi-yau manifoldsCalabi-Yau coomologia orbifoldSettore MAT/03 - Geometriaorbifold cohomologyNonlinear Sciences::Pattern Formation and SolitonsMathematics
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Numerical study of the transverse stability of the Peregrine solution

2020

We generalise a previously published approach based on a multi-domain spectral method on the whole real line in two ways: firstly, a fully explicit 4th order method for the time integration, based on a splitting scheme and an implicit Runge--Kutta method for the linear part, is presented. Secondly, the 1D code is combined with a Fourier spectral method in the transverse variable both for elliptic and hyperbolic NLS equations. As an example we study the transverse stability of the Peregrine solution, an exact solution to the one dimensional nonlinear Schr\"odinger (NLS) equation and thus a $y$-independent solution to the 2D NLS. It is shown that the Peregrine solution is unstable against all…

Mathematics::Analysis of PDEsFOS: Physical sciences010103 numerical & computational mathematics01 natural sciencesStability (probability)spectral approachdispersive blow-upperfectly matched layersymbols.namesakeMathematics - Analysis of PDEsnonlinear Schrodinger equations0103 physical sciencesFOS: MathematicsMathematics - Numerical Analysis0101 mathematics[MATH]Mathematics [math]010306 general physicsNonlinear Sciences::Pattern Formation and SolitonsReal lineVariable (mathematics)Physicsschrodinger-equationsNonlinear Sciences - Exactly Solvable and Integrable SystemsApplied MathematicsMathematical analysisNumerical Analysis (math.NA)Nonlinear systemTransverse planeExact solutions in general relativityFourier transformPeregrine solutionsymbolsExactly Solvable and Integrable Systems (nlin.SI)Spectral methodAnalysis of PDEs (math.AP)
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An application of neural networks to natural scene segmentation

2006

This paper introduces a method for low level image segmentation. Pixels of the image are classified corresponding to their chromatic features.

Mathematics::CombinatoricsArtificial neural networkPixelSegmentation-based object categorizationbusiness.industryComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentationImage segmentationImage (mathematics)Computer Science::Computer Vision and Pattern RecognitionNatural (music)Computer visionChromatic scaleArtificial intelligencebusiness
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Combinatorial Gray codes for classes of pattern avoiding permutations

2007

The past decade has seen a flurry of research into pattern avoiding permutations but little of it is concerned with their exhaustive generation. Many applications call for exhaustive generation of permutations subject to various constraints or imposing a particular generating order. In this paper we present generating algorithms and combinatorial Gray codes for several families of pattern avoiding permutations. Among the families under consideration are those counted by Catalan, Schr\"oder, Pell, even index Fibonacci numbers and the central binomial coefficients. Consequently, this provides Gray codes for $\s_n(\tau)$ for all $\tau\in \s_3$ and the obtained Gray codes have distances 4 and 5.

Mathematics::CombinatoricsFibonacci numberPattern avoiding permutationsGeneral Computer ScienceOrder (ring theory)Generating algorithms94B25Gray codesCombinatorial algorithms05A05; 94B25; 05A15Theoretical Computer ScienceCombinatoricsSet (abstract data type)Constraint (information theory)Gray codePermutation05A05ComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATIONFOS: MathematicsMathematics - CombinatoricsCombinatorics (math.CO)05A15Binomial coefficientComputer Science(all)MathematicsTheoretical Computer Science
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