0000000000323703

AUTHOR

Rachid Sabre

showing 23 related works from this author

Estimation de la densité de la mesure spectrale mixte pour un processus symétrique stable strictement stationnaire

1994

International audience

[PHYS]Physics [physics]densité spectrale processus stable fourier[ MATH ] Mathematics [math][ PHYS ] Physics [physics][ STAT ] Statistics [stat][SPI] Engineering Sciences [physics][MATH] Mathematics [math][STAT] Statistics [stat][PHYS] Physics [physics][STAT]Statistics [stat][SPI]Engineering Sciences [physics][ SPI ] Engineering Sciences [physics][MATH]Mathematics [math]ComputingMilieux_MISCELLANEOUS
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Early detection of disease on leaves by image processing

2013

International audience

[SDV] Life Sciences [q-bio][SDE] Environmental Sciences[SDV]Life Sciences [q-bio][SDE]Environmental Sciences[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal BiologyComputingMilieux_MISCELLANEOUS
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Frequency composition of traction and tillage forces on a mole plough

1997

International audience

[ MATH ] Mathematics [math]stationnary spectral processes Fourier[PHYS]Physics [physics][ PHYS ] Physics [physics][ STAT ] Statistics [stat][SPI] Engineering Sciences [physics][MATH] Mathematics [math][STAT] Statistics [stat][PHYS] Physics [physics][STAT]Statistics [stat][SPI]Engineering Sciences [physics][ SPI ] Engineering Sciences [physics][MATH]Mathematics [math]ComputingMilieux_MISCELLANEOUS
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Measuring vine leaf roughness by image processing

2013

International audience; In precision spraying, spray application efficiency depends on the pesticide application method, the phytosanitary product as well as the leaf surface properties. For environmental and economic reasons, the global trend is to reduce the pesticide application rate of the few approved active substances. Under these constraints, one of the challenges is to improve the efficiency of pesticide application. Different parameters can influence on pesticide application as nozzle types, liquid viscosity and leaf surface. Specific models have been developed showing that the predominant factor for the leaf is the leaf roughness, because it is related on adhesion mechanisms of li…

[SDV] Life Sciences [q-bio][SDE] Environmental SciencesGeneralized Fourier Descriptor[SDV]Life Sciences [q-bio][SDE]Environmental SciencesNeural Network[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal Biologyleaf surface roughnessnonlinear reduction dimensionality methodstexture
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Evolutionary Spectrum for Random Field and Missing Observations

2012

There are innumerable situations where the data observed from a non-stationary random field are collected with missing values. In this work a consistent estimate of the evolutionary spectral density is given where some observations are randomly missing.

Random fieldSpectrum (functional analysis)StatisticsSpectral densityPeriodogramStatistical physicsMissing dataMathematics
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Spectral Density Estimate for Stable Processes Observed with an Additive Error

2018

International audience; In this paper, a symmetric alpha stable process where its spectral representation has an additive error is considered. The error is supposed to be constant. A periodogram as estimator of the spectral density and its rate of convergence are given. In order to give an asymptotically unbiased and consistent estimate of the spectral density, this periodogram is smoothed by an adapted spectral window. The rate of convergence is given.

Health (social science)General Computer ScienceAdditive errorGeneral MathematicsSpectral DensityStable Processes01 natural sciencesEducationStable process[SPI]Engineering Sciences [physics][MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]0103 physical sciencesStatistical physics[MATH]Mathematics [math]PeriodogramGeneral Environmental ScienceMathematics010308 nuclear & particles physicsSpectral windowGeneral EngineeringEstimatorSpectral density[STAT]Statistics [stat]General EnergyRate of convergencePeriodogramConstant (mathematics)[MATH.MATH-SP]Mathematics [math]/Spectral Theory [math.SP]Advanced Science Letters
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Roughness evaluation of vine leaf by image processing

2013

International audience; The study of leaf surface roughness is very important in the domain of precision spraying. It is one of the parameters that allow to reduce costs and losses of phytosanitary prod- ucts and to improve the spray accuracy. Moreover, the leaf roughness is related to adhesion mechanisms of liquid on a surface. It can be used to define leaf nature surface (hy- drophilic/hydrophobic). The main goal of this study is thus to estimate and to follow the evolution of leaf roughness using image processing and computer vision. The develop- ment and application of computer vision for measurement of surface leaf roughness using artificial neural networks will be described. The syste…

[ MATH ] Mathematics [math]0106 biological sciences0209 industrial biotechnologyScanning electron microscope[SDV]Life Sciences [q-bio]Computer Vision[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[MATH] Mathematics [math]02 engineering and technologySurface finishLeaf roughness01 natural sciences[PHYS] Physics [physics][SPI]Engineering Sciences [physics]020901 industrial engineering & automation[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[ SPI ] Engineering Sciences [physics]Surface roughnessComputer vision[MATH]Mathematics [math]ComputingMilieux_MISCELLANEOUS[PHYS]Physics [physics][ PHYS ] Physics [physics]Artificial neural network[STAT]Statistics [stat]Multilayer perceptron[SDE]Environmental SciencesBiological system[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingMaterials science[ STAT ] Statistics [stat][INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[SPI] Engineering Sciences [physics]IASTEDFast Fourier transformNeural NetworkImage processingImage processing[SDV.BV]Life Sciences [q-bio]/Vegetal BiologyTexturelanguage technologies[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingPrecision agriculturebusiness.industry[STAT] Statistics [stat]Precision agricultureArtificial intelligencebusiness010606 plant biology & botany
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Spectral density estimation for stationary stable random fields

1995

International audience

[ MATH ] Mathematics [math]Mathematical optimization[ STAT ] Statistics [stat][SPI] Engineering Sciences [physics][MATH] Mathematics [math]01 natural sciences[PHYS] Physics [physics][SPI]Engineering Sciences [physics]010104 statistics & probability[ SPI ] Engineering Sciences [physics]Applied mathematics[MATH]Mathematics [math]0101 mathematicsComputingMilieux_MISCELLANEOUSMathematics[PHYS]Physics [physics][ PHYS ] Physics [physics]Random fieldApplied MathematicsSpectral density estimation[STAT] Statistics [stat][STAT]Statistics [stat]010101 applied mathematicsDiscrete time and continuous timeVariable kernel density estimationKernel embedding of distributionsKernel (statistics)PeriodogramApplicationes Mathematicae
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Comparison of leaf surface roughness analysis methods by sensitivity to noise analysis

2015

International audience; Surface roughness is of great interest in agricultural spraying because it is used to characterise leaf surface wettability to predict the behaviour of droplets on a leaf surface. In recent years, the use of texture analysis to estimate surface roughness has emerged. In this paper we propose to estimate leaf surface roughness by using an optimisation of the Generalized Fourier Descriptors method. This approach is then compared with two other standard methods in the literature, one based on grey level intensity variation and the other on wavelet decomposition. Since roughness has many definitions and each method is calculated differently, we propose a new approach to …

Surface (mathematics)Materials science[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingGaussianSoil ScienceWavelet decompositionSurface finishLeaf roughnessNoise analysissymbols.namesakeOptics[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingSurface roughnessSensitivity (control systems)Generalized Fourier DescriptorsSensitivity indicatorbusiness.industryOptical roughnessNoiseControl and Systems EngineeringsymbolsWettingBiological systembusinessAgronomy and Crop ScienceIntensity (heat transfer)Food Science
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Missing Observations and Evolutionary Spectrum for Random Fields

2012

International audience

[ MATH ] Mathematics [math][PHYS]Physics [physics][ PHYS ] Physics [physics][ STAT ] Statistics [stat]Evolutionary spactral[SPI] Engineering Sciences [physics]Missing data analysis[MATH] Mathematics [math][STAT] Statistics [stat][PHYS] Physics [physics][STAT]Statistics [stat][SPI]Engineering Sciences [physics][ SPI ] Engineering Sciences [physics][MATH]Mathematics [math]Nonstationary processesComputingMilieux_MISCELLANEOUS
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Leaf surface roughness characterization by image processing

2013

International audience

[SDV] Life Sciences [q-bio][SDE] Environmental Sciencesleaf roughnessprecision agriculturecharacterization of the leaf surface[SDV]Life Sciences [q-bio][SDE]Environmental Sciences[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal BiologyComputingMilieux_MISCELLANEOUSaccurate sprayingtexture analysisspectral analysis
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Vine leaf roughness estimation by image processing

2013

International audience; The application of plant protection product has an important role in agricultural production processes. With current pesticides management, a huge amount of them are applied to worldwide orchards. In precision spraying, spray application efficiency depends on the pesticide application method, the phytosanitary product as well as the leaf surface properties. For environmental and economic reasons, the global trend is to reduce the pesticide application rate of the few approved active substances. Under these constraints, one of the challenges is to improve the efficiency of pesticide application. Different parameters can influence pesticide application such as nozzle t…

Leaf surface roughness[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[SDE.IE]Environmental Sciences/Environmental EngineeringKernel Discriminant AnalysisNeural Network.Neural Network[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[ SDE.IE ] Environmental Sciences/Environmental EngineeringGeneralized Fourier Descriptor[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[SDE.IE] Environmental Sciences/Environmental EngineeringTexture[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
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Evolutionary Spectrum for Random Fields and missing observations

2012

International audience

[STAT]Statistics [stat][PHYS]Physics [physics][ MATH ] Mathematics [math][SPI]Engineering Sciences [physics][ PHYS ] Physics [physics][ STAT ] Statistics [stat][SPI] Engineering Sciences [physics][ SPI ] Engineering Sciences [physics][MATH] Mathematics [math][MATH]Mathematics [math]ComputingMilieux_MISCELLANEOUS[STAT] Statistics [stat][PHYS] Physics [physics]
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Planification expérimentale en agroalimentaire

2006

[SPI] Engineering Sciences [physics]Plan d'expérienceméthode fractionnaire[MATH] Mathematics [math]simplexe[STAT] Statistics [stat][PHYS] Physics [physics]
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Aliasing-Free and Additive Error in Mixed Spectra for Stable Processes. Application: Sound of a Bird just captivated in stress

2022

Consider a symmetric continuous time α stableprocess observed with an additive constant error. Theobjective of this paper is to give a non-parametric estimatorof this error by using discrete observations. As the time ofprocess is continuous and the observations are discrete, weencountered the aliasing phenomenon. Our process sampleis taken in a way to circumvent the difficulty related toaliasing and we smoothed the periodogram by using JacksonKernel. The rate of convergence of this estimator is studiedwhen the spectral density is zero at origin. Few long memoryprocesses are taken here as examples. We have applied ourestimator to the concrete case of modeling noise of a birdcaptured under st…

stable processe[SPI] Engineering Sciences [physics]spectral densityJackson kernel[MATH] Mathematics [math][INFO] Computer Science [cs]
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Ecrits sur les processus aléatoires, Analyse spectrale des processus alpha stables

2012

International audience

[ MATH ] Mathematics [math][STAT]Statistics [stat][PHYS]Physics [physics][SPI]Engineering Sciences [physics][ PHYS ] Physics [physics][ STAT ] Statistics [stat][SPI] Engineering Sciences [physics][ SPI ] Engineering Sciences [physics][MATH] Mathematics [math][MATH]Mathematics [math]ComputingMilieux_MISCELLANEOUS[STAT] Statistics [stat][PHYS] Physics [physics]
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Local Distance and Dempster-Dhafer for Multi-Focus Image Fusion

2022

This work proposes a new method of fusion image using Dempster-Shafer theory and local variability (DST-LV). This method takes into account the behaviour of each pixel with its neighbours. It consists in calculating the quadratic distance between the value of the pixel I (x, y) of each point and the value of all the neighbouring pixels. Local variability is used to determine the mass function defined in DempsterShafer theory. The two classes of Dempster-Shafer theory studied are : the fuzzy part and the focused part. The results of the proposed method are significantly better when comparing them to results of other methods.

[SPI] Engineering Sciences [physics]fusion imagesMulti-focus-imagesDempster-Shafer Theory[MATH] Mathematics [math]local distancefusion images.[INFO] Computer Science [cs]Computer Science::Artificial IntelligenceSignal & Image Processing : An International Journal
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Estimation of the constant measurement error of stable random field

1999

International audience

[PHYS]Physics [physics][ MATH ] Mathematics [math][ PHYS ] Physics [physics][ STAT ] Statistics [stat][SPI] Engineering Sciences [physics][MATH] Mathematics [math][STAT] Statistics [stat][PHYS] Physics [physics][STAT]Statistics [stat]Spectral Analysis[SPI]Engineering Sciences [physics][ SPI ] Engineering Sciences [physics]random fields[MATH]Mathematics [math]ComputingMilieux_MISCELLANEOUS
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Spectral density estimate for alpha-stable p-adic processes

2012

International audience

[PHYS]Physics [physics][ MATH ] Mathematics [math]p_adic numbers[ PHYS ] Physics [physics][ STAT ] Statistics [stat][SPI] Engineering Sciences [physics][MATH] Mathematics [math][STAT] Statistics [stat][PHYS] Physics [physics][STAT]Statistics [stat][SPI]Engineering Sciences [physics]alpha stablespectral density[ SPI ] Engineering Sciences [physics][MATH]Mathematics [math]ComputingMilieux_MISCELLANEOUS
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Multi-focus image fusion using Laplacian Pyramid technique based on Alpha-Stable filter

2019

International audience

[STAT]Statistics [stat][SPI]Engineering Sciences [physics][SPI] Engineering Sciences [physics][MATH.MATH-ST]Mathematics [math]/Statistics [math.ST][MATH] Mathematics [math][MATH]Mathematics [math][MATH.MATH-ST] Mathematics [math]/Statistics [math.ST][SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingComputingMilieux_MISCELLANEOUS[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing[STAT] Statistics [stat]
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Détermination de la texture de la feuille de vigne par imagerie

2013

National audience; Dans le contexte de la pulvérisation de précision, nombreuses sont les recherches menées sur l'optimisation d'utilisation des produits phytosanitaires. L'objectif final étant de réduire de manière significative la quantité d'intrant dans les cultures . Dans ce cadre, les travaux présentés dans cet article s'intéresse particulièrement à l'analyse de l'état de surface foliaire qui présente une part essentielle dans le processus d'adhésion du produit pulvérisé sur la feuille. L'analyse de surface de la feuille est réalisée à travers l'analyse des caractéristiques texturale extraites d'images microscopics. Afin de discriminer les différents cépages et âges des feuilles retenu…

[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Analyse discriminante linéaire et non linéaire[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]TextureDescripteur Généralise de FourierRéseau de neuronessurface foliaire[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
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Plan d’expériences, Méthode de Taguchi

2007

National audience

[PHYS]Physics [physics][ MATH ] Mathematics [math][ PHYS ] Physics [physics][ STAT ] Statistics [stat][SPI] Engineering Sciences [physics][MATH] Mathematics [math][STAT] Statistics [stat][PHYS] Physics [physics][STAT]Statistics [stat]Tagguchi[SPI]Engineering Sciences [physics][ SPI ] Engineering Sciences [physics][MATH]Mathematics [math]ComputingMilieux_MISCELLANEOUS
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Wavelet Decomposition in Laplacian Pyramid for Image Fusion

2016

International audience; The aim of image fusion is to combine information from the set of images to get a single image which contains a more accurate description than any individual source image. While the scene contains objects in different focus due to the limited depth-of-focus of optical lenses in camera then by using image fusion technique we can get an image which has better focus across all area. In this paper, a multifocus image fusion method using combination Laplacian pyramid and wavelet decomposition is proposed. The fusion process contains the following steps: first, the multifocus images are decomposed using Laplacian pyramid into several levels of pyramid. Then at each level o…

[ MATH ] Mathematics [math][STAT]Statistics [stat][PHYS]Physics [physics][SPI]Engineering Sciences [physics][ PHYS ] Physics [physics][ STAT ] Statistics [stat]Computer Science::Computer Vision and Pattern Recognition[ SPI ] Engineering Sciences [physics]laplacian pyramidwavelet decomposition[MATH]Mathematics [math]image fusion
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