Search results for " Statistics"

showing 10 items of 1891 documents

Region-based segmentation on depth images from a 3D reference surface for tree species recognition.

2013

International audience; The aim of the work presented in this paper is to develop a method for the automatic identification of tree species using Terrestrial Light Detection and Ranging (T-LiDAR) data. The approach that we propose analyses depth images built from 3D point clouds corresponding to a 30 cm segment of the tree trunk in order to extract characteristic shape features used for classifying the different tree species using the Random Forest classifier. We will present the method used to transform the 3D point cloud to a depth image and the region based segmentation method used to segment the depth images before shape features are computed on the segmented images. Our approach has be…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingComputer science[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingFeature extractionPoint cloudComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentation[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technology[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Minimum spanning tree-based segmentation[STAT.AP] Statistics [stat]/Applications [stat.AP][INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[ INFO.INFO-TI ] Computer Science [cs]/Image Processing0202 electrical engineering electronic engineering information engineeringSegmentationComputer vision[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing[STAT.AP]Statistics [stat]/Applications [stat.AP]Contextual image classificationbusiness.industry[ STAT.AP ] Statistics [stat]/Applications [stat.AP][INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]020207 software engineeringPattern recognitionImage segmentation15. Life on landdepth image segmentationRandom forestdepth images from 3D point cloudsIEEE[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV][INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]020201 artificial intelligence & image processingsingle tree species recognitionArtificial intelligenceRange segmentationbusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingForest inventory
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Ontology-driven Image Analysis for Histopathological Images

2010

International audience; Ontology-based software and image processing engine must cooperate in new fields of computer vision like microscopy acquisition wherein the amount of data, concepts and processing to be handled must be properly controlled. Within our own platform, we need to extract biological objects of interest in huge size and high-content microscopy images. In addition to specific low-level image analysis procedures, we used knowledge formalization tools and high-level reasoning ability of ontology-based software. This methodology made it possible to improve the expressiveness of the clinical models, the usability of the platform for the pathologist and the sensitivity or sensibi…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputer science[INFO.INFO-IM] Computer Science [cs]/Medical ImagingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage processingOntology (information science)[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]030218 nuclear medicine & medical imaging03 medical and health sciences[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]0302 clinical medicineSoftware[STAT.AP] Statistics [stat]/Applications [stat.AP][INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingDigital image processing[ INFO.INFO-TI ] Computer Science [cs]/Image Processing[INFO.INFO-IM]Computer Science [cs]/Medical ImagingComputer visionRDFImage analysis[STAT.AP]Statistics [stat]/Applications [stat.AP]Information retrieval[ INFO.INFO-IM ] Computer Science [cs]/Medical Imagingbusiness.industry[ STAT.AP ] Statistics [stat]/Applications [stat.AP][INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Usabilitycomputer.file_formatAutomatic image annotation[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV]030220 oncology & carcinogenesis[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]Artificial intelligencebusinesscomputer
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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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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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The pure descent statistic on permutations

2017

International audience; We introduce a new statistic based on permutation descents which has a distribution given by the Stirling numbers of the first kind, i.e., with the same distribution as for the number of cycles in permutations. We study this statistic on the sets of permutations avoiding one pattern of length three by giving bivariate generating functions. As a consequence, new classes of permutations enumerated by the Motzkin numbers are obtained. Finally, we deduce results about the popularity of the pure descents in all these restricted sets. (C) 2017 Elsevier B.V. All rights reserved.

[ MATH ] Mathematics [math]Golomb–Dickman constantDistribution (number theory)PermutationStirling numbers of the first kindStirling number0102 computer and information sciences01 natural sciencesTheoretical Computer ScienceCombinatoricsPermutationComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATIONDiscrete Mathematics and CombinatoricsStirling number[MATH]Mathematics [math]0101 mathematicsPatternsStatisticMathematicsDiscrete mathematicsMathematics::Combinatorics010102 general mathematicsDescentParity of a permutationGray Code010201 computation theory & mathematicsRandom permutation statisticsDyck pathPopularity Fixed NumberDiscrete Mathematics
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Interior Eigenvalue Density of Jordan Matrices with Random Perturbations

2017

International audience; We study the eigenvalue distribution of a large Jordan block subject to a small random Gaussian perturbation. A result by E. B. Davies and M. Hager shows that as the dimension of the matrix gets large, with probability close to 1, most of the eigenvalues are close to a circle.We study the expected eigenvalue density of the perturbed Jordan block in the interior of that circle and give a precise asymptotic description.; Nous étudions la distribution de valeurs propres d’un grand bloc de Jordan soumis à une petite perturbation gaussienne aléatoire. Un résultat de E. B. Davies et M. Hager montre que quand la dimension de la matrice devient grande, alors avec probabilité…

[ MATH ] Mathematics [math]Jordan matrixSpectral theoryGaussian010102 general mathematicsMathematical analysisPerturbation (astronomy)Mathematics::Spectral Theory01 natural sciences010104 statistics & probabilityMatrix (mathematics)symbols.namesakesymbolsRandom perturbations[MATH]Mathematics [math]MSC: 47A10 47B80 47H40 47A550101 mathematicsDivide-and-conquer eigenvalue algorithmSpectral theoryEigenvalue perturbationEigenvalues and eigenvectorsNon-self-adjoint operatorsMathematics
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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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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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Convergence Rates for Persistence Diagram Estimation in Topological Data Analysis

2014

International audience; Computational topology has recently seen an important development toward data analysis, giving birth to the field of topological data analysis. Topological persistence, or persistent homology, appears as a fundamental tool in this field. In this paper, we study topological persistence in general metric spaces, with a statistical approach. We show that the use of persistent homology can be naturally considered in general statistical frameworks and that persistence diagrams can be used as statistics with interesting convergence properties. Some numerical experiments are performed in various contexts to illustrate our results.

[ MATH ] Mathematics [math][STAT.TH] Statistics [stat]/Statistics Theory [stat.TH][ MATH.MATH-AT ] Mathematics [math]/Algebraic Topology [math.AT][STAT.TH]Statistics [stat]/Statistics Theory [stat.TH][MATH.MATH-AT] Mathematics [math]/Algebraic Topology [math.AT][INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG][ STAT.TH ] Statistics [stat]/Statistics Theory [stat.TH]persistent homologytopological data analysis[INFO.INFO-CG] Computer Science [cs]/Computational Geometry [cs.CG][MATH.MATH-AT]Mathematics [math]/Algebraic Topology [math.AT]convergence rates[ INFO.INFO-CG ] Computer Science [cs]/Computational Geometry [cs.CG][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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