Search results for "pattern recognition"

showing 10 items of 2301 documents

Pattern-recognising Polymer Adsorption on Structured Surfaces: Gaussian Polymers vs. Freely Jointed Chains

2014

Abstract Selective adsorption of homopolymers is exploited as a model for pattern recognition. To this end the strong adsorption regime of Gaussian polymers adsorbed on a regularly structured surface is investigated for square and triangular lattices within a discrete Edwards model. The equilibrium behaviour of the specific heat, the gyration tensor and the (nematic) bond order tensor are analysed and compared to the properties for adsorbed freely jointed polymer chains.

chemistry.chemical_classificationQuantitative Biology::BiomoleculesMaterials sciencepattern recognitionGyration tensorPolymerPolymer adsorptionPhysics and Astronomy(all)Bond orderMonte Carlo simulationsCondensed Matter::Soft Condensed MatterPolymer adsorptionCondensed Matter::Materials ScienceAdsorptionchemistryLiquid crystalChemical physicsSelective adsorptionTensorPhysics::Chemical PhysicsPhysics Procedia
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Impact of Wavelet Kernels on Predictive Capability of Radiomic Features: A Case Study on COVID-19 Chest X-ray Images

2023

Radiomic analysis allows for the detection of imaging biomarkers supporting decision-making processes in clinical environments, from diagnosis to prognosis. Frequently, the original set of radiomic features is augmented by considering high-level features, such as wavelet transforms. However, several wavelets families (so called kernels) are able to generate different multi-resolution representations of the original image, and which of them produces more salient images is not yet clear. In this study, an in-depth analysis is performed by comparing different wavelet kernels and by evaluating their impact on predictive capabilities of radiomic models. A dataset composed of 1589 chest X-ray ima…

chest X-ray imagesradiomic featuresSettore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioniwavelet kernelsRadiology Nuclear Medicine and imagingCOVID-19 prognosisComputer Vision and Pattern RecognitionElectrical and Electronic Engineeringmachine learning modelswavelet-derived featurespredictive capabilityComputer Graphics and Computer-Aided Design
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Design of a customized pattern for improving color constancy across camera and illumination changes

2010

International audience; This paper adresses the problem of color constancy on a large image database acquired with varying digital cameras and lighting conditions. Automatic white balance control proposed by an available commercial camera is not sufficient to provide reproducible color classification. A device-independent color representation may be obtained by applying a chromatic adaptation transform, from a calibrated color checker pattern included in the field of view. Instead of using the standard Macbeth color checker, we suggest to select judicious colors to design a customized pattern from contextual information. A comparative study demonstrates that this approach insures a stronger…

chromatic adaptationComputer scienceColor normalizationComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONColor balanceField of view02 engineering and technologyVision control[ 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 medicine0202 electrical engineering electronic engineering information engineeringContextual informationComputer visionColor representationColor constancybusiness.industry[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Color imagingChromatic adaptationchromatic adaptation.color checker design020201 artificial intelligence & image processingArtificial intelligencebusiness
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A Non-Local Mode-I Cohesive Model for Ascending Thoracic Aorta Dissections (ATAD)

2018

This paper presents a non-local interface mechanical model to describe aortic dissection. In this regard, the mode-I debonding problem based on a cohesive zone modeling is endowed with non-local terms to include long-range interactions that are present in multi-layered biological tissue. Such non-local effects are related to the collagen fibers that transmit forces between non-adjacent elements. Numerical simulations are provided with different values of the non-local parameters in order to show the effect of the non-locality during the debonding processes.

cohesive zone modelSettore MED/09 - Medicina InternaMaterials scienceEnergy Engineering and Power Technologydebonding processIndustrial and Manufacturing Engineeringbiomechanicsnon-local effectsArtificial Intelligencemedicine.arterybiomechanics; cohesive zone model; debonding process; non-local effectsmedicineThoracic aortaInstrumentationdebonding proceAortic dissectionRenewable Energy Sustainability and the EnvironmentMode (statistics)BiomechanicsComputer Science Applications1707 Computer Vision and Pattern RecognitionMechanicsBiological tissuemedicine.diseaseNon localCohesive zone modelComputer Networks and Communicationnon-local effectbiomechanicSettore ICAR/08 - Scienza Delle Costruzioni
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Applying fully tensorial ICA to fMRI data

2016

There are two aspects in functional magnetic resonance imaging (fMRI) data that make them awkward to analyse with traditional multivariate methods - high order and high dimension. The first of these refers to the tensorial nature of observations as array-valued elements instead of vectors. Although this can be circumvented by vectorizing the array, doing so simultaneously loses all the structural information in the original observations. The second aspect refers to the high dimensionality along each dimension making the concept of dimension reduction a valuable tool in the processing of fMRI data. Different methods of tensor dimension reduction are currently gaining popUlarity in literature…

computer.software_genre01 natural sciencesTask (project management)010104 statistics & probability03 medical and health sciences0302 clinical medicineDimension (vector space)medicinePreprocessorTensor0101 mathematicsMathematicsta112medicine.diagnostic_testbusiness.industryDimensionality reductionfMRIPattern recognitionIndependent component analysisdataPrincipal component analysisData miningArtificial intelligencefunctional magnetic resonance imaging databusinessFunctional magnetic resonance imagingcomputer030217 neurology & neurosurgery2016 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)
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One and Two Dimensional Convolutional Neural Networks for Seizure Detection Using EEG Signals

2021

Deep learning for the automated detection of epileptic seizures has received much attention during recent years. In this work, one dimensional convolutional neural network (1D-CNN) and two dimensional convolutional neural network (2D-CNN) are simultaneously used on electroencephalogram (EEG) data for seizure detection. Firstly, using sliding windows without overlap on raw EEG to obtain the definite one-dimension time EEG segments (1D-T), and continuous wavelet transform (CWT) for 1D-T signals to obtain the two-dimension time-frequency representations (2D-TF). Then, 1D-CNN and 2D-CNN model architectures are used on 1D-T and 2D-TF signals for automatic classification, respectively. Finally, t…

convolutional neural networks (CNN)Computer scienceseizure detection02 engineering and technologyneuroverkotElectroencephalographyConvolutional neural network0202 electrical engineering electronic engineering information engineeringmedicineEEGContinuous wavelet transformSignal processingArtificial neural networkmedicine.diagnostic_testbusiness.industryelectroencephalogram (EEG)signaalinkäsittelyDeep learningtime-frequency representationtideep learningsignaalianalyysi020206 networking & telecommunicationsPattern recognitionkoneoppiminenBenchmark (computing)020201 artificial intelligence & image processingArtificial intelligencebusinessepilepsia
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Curvelet-based method for orientation estimation of particles from optical images

2014

A method based on the curvelet transform is introduced to estimate the orientation distribution from two-dimensional images of small anisotropic particles. Orientation of fibers in paper is considered as a particular application of the method. Theoretical aspects of the suitability of this method are discussed and its efficiency is demonstrated with simulated and real images of fibrous systems. Comparison is made with two traditionally used methods of orientation analysis, and the new curvelet-based method is shown to perform better than these tradi- tional methods. peerReviewed

curveletmultiscaleComputer Science::Computer Vision and Pattern Recognitionanisotropicorientationfiber
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Predicting the availability of users' devices in Decentralized Online Social Networks

2018

The understanding of the user temporal behavior is a crucial aspect for all those systems that rely on user resources for daily operations, such as decentralized online social networks (DOSNs). Indeed, DOSNs exploit the devices of their users to take on and share the tasks needed to provide services such as storing the published data. In the last years, the increasing popularity of DOSN services has changed the way of how people interact with each other by enabling users to connect to these services at any time by using their personal devices (such as notebooks or smartphones). As a result, the availability of data in these systems is strongly affected (or reflected) by the temporal behavio…

decentralized online social networkdecentralized online social networksavailability predictionComputer Networks and CommunicationComputational Theory and Mathematicsavailabilityuser behaviorComputer Science Applications1707 Computer Vision and Pattern Recognitionpredictionavailability prediction; decentralized online social networks; user behaviorSoftwareTheoretical Computer Science
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Détection robuste de mouvement par histogrammes quasi‐continus

2012

National audience; Dans cet article, nous proposons d'utiliser la représentation de distributions de valeurs par histogrammes quasi-continus pour réaliser une détection temps réel de mouvement dans une séquence d'images. Nous comparons les résultats de cette détection à deux méthodes de référence de la littérature.

densityvision[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]motion detection[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Quasi-continuous histogram[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]image processing
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Performance of $b$-Jet Identification in the ATLAS Experiment

2016

We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF, DNSRC and Lundbeck Foundation, Denmark; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT an…

detector-systems performancePerformance of High Energy Physics Detectorsecondary [vertex]Elementary particle01 natural sciencesPARTONlaw.inventionSubatomär fysikCHANNELcluster findingscattering [p p]impact parameterGeneralLiterature_REFERENCE(e.g.dictionariesencyclopediasglossaries)протон-протонные столкновенияQBLarge detector-systems performanceHigh energy physics detectorLarge Hadron ColliderLarge detector systems for particle and astroparticle physics; Large detector-systems performance; Pattern recognition cluster finding calibration and fitting methods; Performance of High Energy Physics Detectors; Instrumentation; Mathematical Physicstrack data analysisQUARK PAIR PRODUCTIONbottom [jet]CERN LHC CollPattern recognition cluster finding calibration and fitting method7000 GeV-cmscolliding beams [p p]performanceHADRONIC COLLISIONSCiências Naturais::Ciências FísicasLarge detectorFitting methodHigh energy physicATLAS LHC High Energy Physics510 MathematicsmuonDISTRIBUTIONSUncertainty analysis Astroparticle physicHigh Energy Physics010306 general physicsSystematic uncertainties AlgorithmsAstroparticle physicsCalibration and fitting methodsScience & Technology010308 nuclear & particles physicsLarge detector systems for particle and astroparticle physicsParticle acceleratorRangingPerformance of High Energy PhysicsCOLLIDERScorrelationExperimental High Energy PhysicsPerformance of High Energy Physics DetectorshadronATLAS детекторБольшой адронный коллайдерcharm [jet]Elementary particleHigh Energy Physics - ExperimentHigh Energy Physics - Experiment (hep-ex)lawSubatomic Physics[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]Detectors and Experimental TechniquesInstrumentationUncertainty analysisMathematical PhysicsPhysicsPattern recognition cluster finding calibration and fitting methods4. EducationATLAS experimentSettore FIS/01 - Fisica SperimentaleDetectorsflavor [jet]calibration and fitting methodsATLASLarge Hadron ColliderLarge detector systems for particle and astroparticle physics; Large; detector-systems performance; Pattern recognition cluster finding; calibration and fitting methods; Performance of High Energy Physics; Detectors; PRODUCTION CROSS-SECTION; QUARK PAIR PRODUCTION; ROOT-S=7 TEV; PARTON; DISTRIBUTIONS; HADRONIC COLLISIONS; MATRIX-ELEMENTS; LHC; COLLIDERS; DETECTOR; CHANNEL8. Economic growthCalibrationparticle identification [bottom]LHCImpact parameterParticle Physics - ExperimentParticle physicsdata analysis method530 Physics:Ciências Físicas [Ciências Naturais]FOS: Physical sciences530MATRIX-ELEMENTSparticle identification [charm]on-line [trigger]Pattern recognition0103 physical sciencesComplementary methodddc:610DETECTORROOT-S=7 TEVCluster findingFísicaLarge detector systems for particle and astroparticle physics; Large detector-systems performance; Pattern recognition cluster finding calibration and fitting methods; Performance of High Energy Physics DetectorsPattern recognition systemcalibrationtracksPRODUCTION CROSS-SECTIONefficiencyHadronLarge detector systems for particle and astroparticle physicLargeHigh Energy Physics::ExperimentStatistical correlationstatisticalexperimental results
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