Search results for "Computer Vision and Pattern Recognition"

showing 10 items of 997 documents

Internally Contracted Multireference Coupled Cluster Calculations with a Spin-Free Dirac-Coulomb Hamiltonian: Application to the Monoxides of Titaniu…

2017

We combine internally contracted multireference coupled cluster theory with a four-component treatment of scalar-relativistic effects based on the spin-free Dirac–Coulomb Hamiltonian. This strategy allows for a rigorous treatment of static and dynamic correlation as well as scalar-relativistic effects, which makes it viable to describe molecules containing heavy transition elements. The use of a spin-free formalism limits the impact of the four-component treatment on the computational cost to the non-rate-determining steps of the calculations. We apply the newly developed method to the lowest singlet and triplet states of the monoxides of titanium, zirconium, and hafnium and show how the in…

Zirconium010304 chemical physicsElectronic correlationComputer Science Applications1707 Computer Vision and Pattern Recognition; Physical and Theoretical Chemistrychemistry.chemical_elementComputer Science Applications1707 Computer Vision and Pattern RecognitionElectronic structure010402 general chemistry01 natural sciences0104 chemical sciencesComputer Science ApplicationsHafniumsymbols.namesakeCoupled clusterchemistry0103 physical sciencessymbolsSinglet statePhysics::Chemical PhysicsAtomic physicsPhysical and Theoretical ChemistryRelativistic quantum chemistryHamiltonian (quantum mechanics)
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Chemical Bath Deposition as a Simple Way to Grow Isolated and Coalesced ZnO Nanorods for Light-Emitting Diodes Fabrication

2018

A way to grow and characterize isolated and coalesced ZnO nanorods on $p$ -GaN/sapphire structure is presented. Chemical bath deposition can be used to grow ZnO nanorods of device-quality, simply controlling the duration time of the growth process and the concentration of the nutrient solution in the bath. Increasing the duration of the process, as well as the concentration of the solution, leads to compact and sound layers instead of separated nanorods. However, too high concentrations stop the growth process. Light-emitting diodes fabricated on these ZnO-p-GaN heterostructure have a peak of electroluminescence at 400 nm and exhibit interesting electrical and optical properties. Optical po…

ZnO nanorodMaterials scienceFabricationRenewable Energy Sustainability and the Environmentbusiness.industryEnergy Engineering and Power TechnologyZnO-p-GaN heterojunction-based LEDComputer Science Applications1707 Computer Vision and Pattern RecognitionHeterojunctionElectroluminescenceSettore ING-INF/01 - ElettronicaIndustrial and Manufacturing Engineeringlaw.inventionchemical bath depositionComputer Networks and CommunicationArtificial IntelligencelawSapphireOptoelectronicsNanorodbusinessInstrumentationLayer (electronics)Chemical bath depositionLight-emitting diode2018 IEEE 4th International Forum on Research and Technology for Society and Industry (RTSI)
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Weighted Adaptive Neighborhood HypergraphPartitioning for Image Segmentation

2005

International audience; The aim of this paper is to present an improvement of a previously published algorithm. The proposed approach is performed in two steps. In the first step, we generate the Weighted Adaptive Neighborhood Hypergraph (WAINH) of the given gray-scale image. In the second step, we partition the WAINH using a multilevel hypergraph partitioning technique. To evaluate the algorithm performances, experiments were carried out on medical and natural images. The results show that the proposed segmentation approach is more accurate than the graph based segmentation algorithm using normalized cut criteria.Key words hypergraph, neighborhood hypergraph, hypergraph partitioning, image…

[ INFO ] Computer Science [cs]Computer Science::Computer Vision and Pattern RecognitionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[INFO]Computer Science [cs][INFO] Computer Science [cs]MathematicsofComputing_DISCRETEMATHEMATICS
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Neighborhood Hypergraph Partitioning for Image Segmentation

2005

International audience; The aim of this paper is to introduce a multilevel neighborhoodhypergraph partitioning for image segmentation. Our proposedapproach uses the image neighborhood hypergraph model introduced inour last works and the algorithm of multilevel hypergraphpartitioning introduced by George Karypis. To evaluate the algorithmperformance, experiments were carried out on a group of gray scaleimages. The results show that the proposed segmentation approachfind the region properly from images as compared to imagesegmentation algorithm using normalized cut criteria.Key words :Graph, Hypergraph, Neighborhood hypergraph, multilevel hypergraph partitioning, image segmentation, edge dete…

[ INFO ] Computer Science [cs]Computer Science::Computer Vision and Pattern RecognitionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[INFO]Computer Science [cs][INFO] Computer Science [cs]MathematicsofComputing_DISCRETEMATHEMATICS
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Automated Characterization of Mouth Activity for Stress and Anxiety Assessment

2016

International audience; Non-verbal information portrayed by human facial expression, apart from emotional cues also encompasses information relevant to psychophysical status. Mouth activities in particular have been found to correlate with signs of several conditions; depressed people smile less, while those in fatigue yawn more. In this paper, we present a semi-automated, robust and efficient algorithm for extracting mouth activity from video recordings based on Eigen-features and template-matching. The algorithm was evaluated for mouth openings and mouth deformations, on a minimum specification dataset of 640x480 resolution and 15 fps. The extracted features were the signals of mouth expa…

[ INFO ] Computer Science [cs]Computer scienceSpeech recognitionFeature extractionautomatic assessmentComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage processing02 engineering and technologymouth gesture recognition[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Yawn[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Correlation03 medical and health sciencesstress0302 clinical medicineRobustness (computer science)Stress (linguistics)[ INFO.INFO-TI ] Computer Science [cs]/Image Processing0202 electrical engineering electronic engineering information engineeringmedicine[INFO]Computer Science [cs][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]Facial expression[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]anxietyimage processingRecognition[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV][SPI.OPTI]Engineering Sciences [physics]/Optics / PhotonicAnxiety020201 artificial intelligence & image processing[ SPI.OPTI ] Engineering Sciences [physics]/Optics / Photonicmedicine.symptom030217 neurology & neurosurgery
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Maximum likelihood difference scaling of image quality in compression-degraded images.

2007

International audience; Lossy image compression techniques allow arbitrarily high compression rates but at the price of poor image quality. We applied maximum likelihood difference scaling to evaluate image quality of nine images, each compressed via vector quantization to ten different levels, within two different color spaces, RGB and CIE 1976 L(*)a(*)b(*). In L(*)a(*)b(*) space, images could be compressed on average by 32% more than in RGB space, with little additional loss in quality. Further compression led to marked perceptual changes. Our approach permits a rapid, direct measurement of the consequences of image compression for human observers.

[ INFO ] Computer Science [cs]Image qualityColorImage processing[INFO] Computer Science [cs]Color space050105 experimental psychology03 medical and health sciences0302 clinical medicineOpticsImage Processing Computer-Assisted[INFO]Computer Science [cs]0501 psychology and cognitive sciences[SDV.MHEP.OS]Life Sciences [q-bio]/Human health and pathology/Sensory OrgansImage resolutionMathematicsColor imagebusiness.industry05 social sciencesVector quantizationData CompressionAtomic and Molecular Physics and OpticsElectronic Optical and Magnetic Materials[SDV.MHEP.OS] Life Sciences [q-bio]/Human health and pathology/Sensory Organs[ SDV.MHEP.OS ] Life Sciences [q-bio]/Human health and pathology/Sensory OrgansRGB color modelComputer Vision and Pattern RecognitionArtifactsbusiness030217 neurology & neurosurgeryImage compression
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Cardiac motion tracking using a deformable 2D-mesh modeling

2001

International audience; Abstract: The work reported here deals with movement tracking in sequences of medical images in order to quantify the general movements and deformations of the heart For this purpose, we partition the first image into triangular patches in order that each object of the image corresponds to a set of triangles. Then, the nodes of the mesh are tracked across the image sequence giving a mesh which warps with the images. The method is applied to cardiac image sequences where the study of the deformation of the triangles is applied to the determination of the movement of the ventricles

[ INFO.INFO-IM ] Computer Science [cs]/Medical ImagingMovement (music)business.industryComputer sciencePhysics::Medical Physics[INFO.INFO-IM] Computer Science [cs]/Medical ImagingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technologyImage segmentationDeformation (meteorology)Tracking (particle physics)030218 nuclear medicine & medical imagingImage (mathematics)Set (abstract data type)03 medical and health sciences0302 clinical medicineComputer Science::Computer Vision and Pattern Recognition0202 electrical engineering electronic engineering information engineeringMedical imaging[INFO.INFO-IM]Computer Science [cs]/Medical Imaging020201 artificial intelligence & image processingComputer visionArtificial intelligencebusinessComputingMethodologies_COMPUTERGRAPHICS
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OPTIMIZATIONS FOR TENSORIAL BERNSTEIN–BASED SOLVERS BY USING POLYHEDRAL BOUNDS

2010

The tensorial Bernstein basis for multivariate polynomials in n variables has a number 3n of functions for degree 2. Consequently, computing the representation of a multivariate polynomial in the tensorial Bernstein basis is an exponential time algorithm, which makes tensorial Bernstein-based solvers impractical for systems with more than n = 6 or 7 variables. This article describes a polytope (Bernstein polytope) with a number of faces, which allows to bound a sparse, multivariate polynomial expressed in the canonical basis by solving several linear programming problems. We compare the performance of a subdivision solver using domain reductions by linear programming with a solver using a c…

[ INFO.INFO-NA ] Computer Science [cs]/Numerical Analysis [cs.NA]Linear programmingPolytopeBernstein polynomials01 natural sciencesSimplex algorithmApplied mathematicssimplex algorithm0101 mathematicsMathematicsDiscrete mathematicsBasis (linear algebra)Applied Mathematics010102 general mathematicssubdivision solverlinear programmingalgebraic systemsQuadratic function[INFO.INFO-NA]Computer Science [cs]/Numerical Analysis [cs.NA]Solver1991 Mathematics Subject Classification: 14Q15 14Q20 65G40Bernstein polynomialComputer Science Applications010101 applied mathematicsModeling and SimulationStandard basisGeometry and TopologyComputer Vision and Pattern RecognitionSoftwareInternational Journal of Shape Modeling
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Comparison of Bathymetric estimation using different satellite images in coastal sea waters

2009

Bathymetric estimation can be obtained from multispectral satellite images for shallow waters. The method is based on the rotation of a pair of spectral bands. One of the resulting images is depth-dependent. Therefore several pixels corresponding to different depths are required to numerically evaluate the linear relation between the pixel values and the real depth for a training area. The aim of this study is to compare, for one bathymetric estimation method and one mesotrophic site, the results of depth estimation with a large panel of satellite and aerial images: CASI, QUICKBIRD, CHRIS PROBA, ETM, HYPERION and MeRIS. For each image the pair of spectral bands chosen to compute the bathyme…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing010504 meteorology & atmospheric sciencesPixelAerial survey[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingMultispectral image0211 other engineering and technologies02 engineering and technologySpectral bands01 natural sciencesMultispectral pattern recognition[SPI]Engineering Sciences [physics][INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingComputer Science::Computer Vision and Pattern RecognitionGeneral Earth and Planetary SciencesBathymetry14. Life underwaterQuantization (image processing)Image resolution[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingGeologyComputingMilieux_MISCELLANEOUS021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensing
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Reaction-Diffusion Network For Geometric Multiscale High Speed Image Processing

2010

International audience; In the framework of heavy mid-level processing for high speed imaging, a nonlinear bi-dimensional network is proposed, allowing the implementation of active curve algorithms. Usually this efficient type of algorithm is prohibitive for real-time image processing due to its calculus charge and the inadequate structure for the use of serial or parallel architectures. Another kind of implementation philosophy is proposed here, by considering the active curve generated by a propagation phenomenon inspired from biological modeling. A programmable nonlinear reaction-diffusion system is proposed under front control and technological constraints. Geometric multiscale processin…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingComputer scienceNonlinear signal processingImage processing02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingType (model theory)Multiscale geometryComputational scienceImage analysisNonlinear signal processing[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingReaction–diffusion systemDigital image processing0202 electrical engineering electronic engineering information engineeringComputer visionStructure (mathematical logic)Biological modelingbusiness.industry020208 electrical & electronic engineeringNonlinear systemSignal Processing020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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