Search results for "Function"

showing 10 items of 14432 documents

An evaluation framework and a benchmark for multi/hyperspectral image compression

2011

International audience; This paper benchmarks three multi/hyperspectral image compression approaches: the classic Multi-2D compression approach and two different implementations of 3D approach (Full 3D and Hybrid). All approaches are combined with a spectral PCA decorrelation stage to optimize performance. These three compression approaches are compared within a larger comparison framework than the conventionally used PSNR, which includes eight metrics divided into three families. The comparison is carried out with regard to variations in bitrates, spatial, and spectral dimensions variations of images. The time and memory consumption difference between the three approaches is also discussed…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingComputer sciencebusiness.industryMultispectral image0211 other engineering and technologiesPattern recognition02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingcompressionwaveletsWavelet[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingCompression (functional analysis)Hyperspectral image compression0202 electrical engineering electronic engineering information engineeringBenchmark (computing)020201 artificial intelligence & image processingArtificial intelligencebusinessDecorrelation[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingMulti/hyperspectral images021101 geological & geomatics engineeringImage compression
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Spatial correction in dynamic photon emission by affine transformation matrix estimation

2014

International audience; Photon emission microscopy and Time Resolved Imaging have proved their efficiency for defect localization on VLSI. A common process to find defect candidate locations is to draw a comparison between acquisitions on a normally working device and a faulty one. In order to be accurate and meaningful, this method requires that the acquisition scene remains the same between the two parts. In practice, it can be difficult to set. In this paper, a method to correct position by affine matrix transformation is suggested. It is based on image features detection, description and matching and affine transformation estimation.

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingMatching (graph theory)Computer science[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[SPI.NANO] Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingPosition (vector)020204 information systems0202 electrical engineering electronic engineering information engineeringComputer vision[SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingVery-large-scale integrationHarris affine region detectorbusiness.industryProcess (computing)Affine shape adaptationTransformation (function)020201 artificial intelligence & image processing[ SPI.NANO ] Engineering Sciences [physics]/Micro and nanotechnologies/MicroelectronicsArtificial intelligenceAffine transformationbusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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An optimized algorithm of image stitching in the case of a multi-modal probe for monitoring the evolution of scars

2013

International audience; We propose a new system that makes possible to monitor the evolution of scars after the excision of a tumorous dermatosis. The hardware part of this system is composed of a new optical innovative probe with which two types of images can be acquired simultaneously: an anatomic image acquired under a white light and a functional one based on autofluorescence from the protoporphyrin within the cancer cells. For technical reasons related to the maximum size of the area covered by the probe, acquired images are too small to cover the whole scar. That is why a sequence of overlapping images is taken in order to cover the required area. The main goal of this paper is to des…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingMatching (graph theory)Panorama[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-invariant feature transform[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technologyautofluorescence010501 environmental sciences01 natural sciencesImage stitching[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processingstitchingmulti-modal probe0202 electrical engineering electronic engineering information engineeringComputer visionProjection (set theory)[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing0105 earth and related environmental sciencesbusiness.industryFluorescenceScars evolutionmonitoringAutofluorescenceTransformation (function)020201 artificial intelligence & image processingArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingAlgorithmSPIE Proceedings
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Kolmogorov Superposition Theorem and Wavelet Decomposition for Image Compression

2009

International audience; Kolmogorov Superposition Theorem stands that any multivariate function can be decomposed into two types of monovariate functions that are called inner and external functions: each inner function is associated to one dimension and linearly combined to construct a hash-function that associates every point of a multidimensional space to a value of the real interval $[0,1]$. These intermediate values are then associated by external functions to the corresponding value of the multidimensional function. Thanks to the decomposition into monovariate functions, our goal is to apply this decomposition to images and obtain image compression. We propose a new algorithm to decomp…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing010102 general mathematicsMathematical analysisWavelet transform02 engineering and technologyFunction (mathematics)[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingSuperposition theorem01 natural sciencesWavelet packet decompositionWavelet[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV]Dimension (vector space)[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV][ INFO.INFO-TI ] Computer Science [cs]/Image Processing0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingPoint (geometry)0101 mathematics[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingImage compressionMathematics
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Kolmogorov Superposition Theorem and Its Application to Multivariate Function Decompositions and Image Representation

2008

International audience; In this paper, we present the problem of multivariate function decompositions into sums and compositions of monovariate functions. We recall that such a decomposition exists in the Kolmogorov's superposition theorem, and we present two of the most recent constructive algorithms of these monovariate functions. We first present the algorithm proposed by Sprecher, then the algorithm proposed by Igelnik, and we present several results of decomposition for gray level images. Our goal is to adapt and apply the superposition theorem to image processing, i.e. to decompose an image into simpler functions using Kolmogorov superpositions. We synthetise our observations, before …

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingImage processing[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technologySuperposition theorem01 natural sciences[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[ INFO.INFO-TI ] Computer Science [cs]/Image ProcessingComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATION0202 electrical engineering electronic engineering information engineeringApplied mathematics0101 mathematics[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingMathematicsDiscrete mathematicsSignal processingArtificial neural network010102 general mathematicsApproximation algorithmSpline (mathematics)[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV]Kolmogorov structure function[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]020201 artificial intelligence & image processingHypercube[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing2008 IEEE International Conference on Signal Image Technology and Internet Based Systems
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Sobolev and bounded variation functions on metric measure spaces

2014

International audience

[ MATH ] Mathematics [math]DifferentiabilityEquationsSets010102 general mathematicsTransport[MATH] Mathematics [math]01 natural sciencesDerivationsFine PropertiesFinite Perimeter010104 statistics & probabilityRicci Curvature BoundsLipschitz Functions0101 mathematics[MATH]Mathematics [math]InequalitiesComputingMilieux_MISCELLANEOUS
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Computational approach to compact Riemann surfaces

2017

International audience; A purely numerical approach to compact Riemann surfaces starting from plane algebraic curves is presented. The critical points of the algebraic curve are computed via a two-dimensional Newton iteration. The starting values for this iteration are obtained from the resultants with respect to both coordinates of the algebraic curve and a suitable pairing of their zeros. A set of generators of the fundamental group for the complement of these critical points in the complex plane is constructed from circles around these points and connecting lines obtained from a minimal spanning tree. The monodromies are computed by solving the defining equation of the algebraic curve on…

[ MATH ] Mathematics [math]Fundamental groupEquations[PHYS.MPHY]Physics [physics]/Mathematical Physics [math-ph]Holomorphic functionGeneral Physics and AstronomyFOS: Physical sciences010103 numerical & computational mathematics01 natural sciencessymbols.namesakeMathematics - Algebraic Geometrynumerical methodsFOS: MathematicsSpectral Methods0101 mathematics[MATH]Mathematics [math]Algebraic Geometry (math.AG)Mathematical PhysicsMathematicsCurvesKadomtsev-Petviashvili equationCollocationNonlinear Sciences - Exactly Solvable and Integrable SystemsPlane (geometry)Applied MathematicsRiemann surface010102 general mathematicsMathematical analysisStatistical and Nonlinear PhysicsMathematical Physics (math-ph)Methods of contour integrationHyperelliptic Theta-FunctionsRiemann surfacessymbolsDispersion Limit[ PHYS.MPHY ] Physics [physics]/Mathematical Physics [math-ph]Algebraic curveExactly Solvable and Integrable Systems (nlin.SI)Complex plane
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Characterizations of convex approximate subdifferential calculus in Banach spaces

2016

International audience; We establish subdifferential calculus rules for the sum of convex functions defined on normed spaces. This is achieved by means of a condition relying on the continuity behaviour of the inf-convolution of their corresponding conjugates, with respect to any given topology intermediate between the norm and the weak* topologies on the dual space. Such a condition turns out to also be necessary in Banach spaces. These results extend both the classical formulas by Hiriart-Urruty and Phelps and by Thibault.

[ MATH ] Mathematics [math]Mathematics::Functional AnalysisApproximate subdifferentialDual spaceConvex functionsApplied MathematicsGeneral MathematicsBanach spaceUniformly convex spaceSubderivativeApproximate variational principleCalculus rulesLocally convex topological vector spaceCalculusInterpolation spaceMSC: Primary 49J53 52A41 46N10[MATH]Mathematics [math]Reflexive spaceLp spaceMathematics
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Surfaces of minimal degree of tame representation type and mutations of Cohen–Macaulay modules

2017

We provide two examples of smooth projective surfaces of tame CM type, by showing that any parameter space of isomorphism classes of indecomposable ACM bundles with fixed rank and determinant on a rational quartic scroll in projective 5-space is either a single point or a projective line. For surfaces of minimal degree and wild CM type, we classify rigid Ulrich bundles as Fibonacci extensions. For the rational normal scrolls S(2,3) and S(3,3), a complete classification of rigid ACM bundles is given in terms of the action of the braid group in three strands.

[ MATH ] Mathematics [math]Pure mathematicsFibonacci numberGeneral MathematicsType (model theory)Rank (differential topology)Commutative Algebra (math.AC)01 natural sciencesMathematics - Algebraic GeometryACM bundlesVarieties of minimal degreeMathematics::Algebraic Geometry0103 physical sciencesFOS: MathematicsMathematics (all)Rings0101 mathematics[MATH]Mathematics [math]Algebraic Geometry (math.AG)MathematicsDiscrete mathematics14F05 13C14 14J60 16G60010102 general mathematicsVarietiesMCM modulesACM bundles; MCM modules; Tame CM type; Ulrich bundles; Varieties of minimal degree; Mathematics (all)Ulrich bundlesMathematics - Commutative AlgebraQuintic functionElliptic curveTame CM typeProjective lineBundles010307 mathematical physicsIsomorphismIndecomposable moduleMSC: 14F05; 13C14; 14J60; 16G60
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Categorical action of the extended braid group of affine type $A$

2017

Using a quiver algebra of a cyclic quiver, we construct a faithful categorical action of the extended braid group of affine type A on its bounded homotopy category of finitely generated projective modules. The algebra is trigraded and we identify the trigraded dimensions of the space of morphisms of this category with intersection numbers coming from the topological origin of the group.

[ MATH ] Mathematics [math]Pure mathematicsGeneral MathematicsCategorificationBraid groupGeometric intersection01 natural sciencesMathematics - Geometric TopologyMorphismMathematics::Category TheoryQuiverMathematics - Quantum Algebra0103 physical sciencesFOS: MathematicsQuantum Algebra (math.QA)Representation Theory (math.RT)0101 mathematics[MATH]Mathematics [math]MathematicsHomotopy categoryGroup (mathematics)Applied Mathematics010102 general mathematicsQuiverBraid groupsGeometric Topology (math.GT)16. Peace & justiceCategorificationCategorical actionBounded functionMSC: 20F36 18E30 57M99 13D99010307 mathematical physicsAffine transformationMathematics - Representation Theory
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