Search results for "Signal and Image processing"

showing 10 items of 454 documents

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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Architecture-Driven Level Set Optimization: From Clustering to Sub-pixel Image Segmentation

2016

Thanks to their effectiveness, active contour models (ACMs) are of great interest for computer vision scientists. The level set methods (LSMs) refer to the class of geometric active contours. Comparing with the other ACMs, in addition to subpixel accuracy, it has the intrinsic ability to automatically handle topological changes. Nevertheless, the LSMs are computationally expensive. A solution for their time consumption problem can be hardware acceleration using some massively parallel devices such as graphics processing units (GPUs). But the question is: which accuracy can we reach while still maintaining an adequate algorithm to massively parallel architecture? In this paper, we attempt to…

Level set methodComputer science0211 other engineering and technologiesInitialization02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingLevel setgraphics processing units0202 electrical engineering electronic engineering information engineeringLevel set methodComputer visionElectrical and Electronic EngineeringCluster analysisMassively parallelimage segmentation021101 geological & geomatics engineeringActive contour modelhybrid CPU-GPU architecturebusiness.industryImage segmentationSubpixel renderingComputer Science ApplicationsHuman-Computer InteractionControl and Systems EngineeringHardware acceleration020201 artificial intelligence & image processingArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingSoftwareInformation Systems
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Weld pool surface temperature measurement from polarization state of thermal emission

2014

This paper presents a passive polarimetry method using a division of aperture optical device in order to measure the temperature distribution at the weld pool surface. Thermal emission from a hot liquid metal was investigated at a near-infrared wavelength corresponding to a blind spectral window of a helium plasma generated during gas tungsten arc welding process. The refractive index of liquid metal and the surface radiance are deduced from the polarisation state of thermal emissions. Based upon the knowledge of both characteristics, the temperature distribution can be calculated. Conseil Régional de Bourgogne

Liquid metalMaterials scienceMatériaux [Sciences de l'ingénieur][ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[SPI.OPTI] Engineering Sciences [physics]/Optics / PhotonicStokes imaging[ SPI.MAT ] Engineering Sciences [physics]/Materials02 engineering and technology01 natural sciencesTemperature measurementTraitement du signal et de l'image [Informatique][SPI.MAT]Engineering Sciences [physics]/Materials010309 opticsOptics[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing0103 physical sciencesThermal[ INFO.INFO-TI ] Computer Science [cs]/Image Processingweld pool temperature[SPI.GPROC]Engineering Sciences [physics]/Chemical and Process EngineeringElectrical and Electronic EngineeringInstrumentationComputingMilieux_MISCELLANEOUSpolarimetrygas tungsten arcTraitement des images [Informatique]business.industryGas tungsten arc welding[ SPI.GPROC ] Engineering Sciences [physics]/Chemical and Process Engineering021001 nanoscience & nanotechnologyWavelengthpolarimetry;[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]RadianceWeld pool[SPI.OPTI]Engineering Sciences [physics]/Optics / Photonic[ SPI.OPTI ] Engineering Sciences [physics]/Optics / Photonic0210 nano-technologybusinessRefractive index
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Video-Based Depression Detection Using Local Curvelet Binary Patterns in Pairwise Orthogonal Planes

2016

International audience; Depression is an increasingly prevalent mood disorder. This is the reason why the field of computer-based depression assessment has been gaining the attention of the research community during the past couple of years. The present work proposes two algorithms for depression detection, one Frame-based and the second Video-based, both employing Curvelet transform and Local Binary Patterns. The main advantage of these methods is that they have significantly lower computational requirements, as the extracted features are of very low dimensionality. This is achieved by modifying the previously proposed algorithm which considers Three-Orthogonal-Planes, to only Pairwise-Ort…

Local binary patternsFeature extractionVideo Recording02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingMachine learningcomputer.software_genreField (computer science)0502 economics and business0202 electrical engineering electronic engineering information engineeringCurveletHumansDiagnosis Computer-Assisted[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingbusiness.industryDepression05 social sciencesReproducibility of ResultsPattern recognitionActive appearance modelFaceBenchmark (computing)020201 artificial intelligence & image processingPairwise comparisonArtificial intelligencebusinessPsychologycomputer[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing050203 business & managementAlgorithmsCurse of dimensionality
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Comparison of different segmentation approaches without using gold standard. Application to the estimation of the left ventricle ejection fraction fr…

2011

International audience; A statistical method is proposed to compare several estimates of a relevant clinical parameter when no gold standard is available. The method is illustrated by considering the left ventricle ejection fraction derived from cardiac magnetic resonance images and computed using seven approaches with different degrees of automation. The proposed method did not use any a priori regarding with the reliability of each method and its degree of automation. The results showed that the most accurate estimates of the ejection fraction were obtained using manual segmentations, followed by the semiautomatic methods, while the methods with the least user input yielded the least accu…

MESH : Ventricular Function Left[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technologyMESH: Regression AnalysisVentricular Function LeftArticleMESH: Ventricular Function Left030218 nuclear medicine & medical imagingMESH: Magnetic Resonance Imaging03 medical and health sciencesMESH : Heart0302 clinical medicine[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingMESH : Magnetic Resonance ImagingMESH : Regression Analysis0202 electrical engineering electronic engineering information engineeringHumansMedicineSegmentation[ SDV.IB ] Life Sciences [q-bio]/BioengineeringReliability (statistics)[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing[SDV.IB] Life Sciences [q-bio]/BioengineeringEjection fractionMESH: Humansbusiness.industryMESH : HumansHeartRegression analysisPattern recognitionImage segmentationGold standard (test)Magnetic Resonance ImagingMESH: Heartmedicine.anatomical_structureVentricleRegression AnalysisA priori and a posteriori020201 artificial intelligence & image processing[SDV.IB]Life Sciences [q-bio]/BioengineeringArtificial intelligencebusinessNuclear medicine[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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Normal Reference Ranges for Echocardiography: Rationale, study design, and methodology (NORRE Study)

2013

International audience; BACKGROUND: Availability of normative reference values for cardiac chamber dimensions, volumes, mass, and function is a prerequisite for the accurate application of echocardiography for both clinical and research purposes. However, due to the lack of consistency in current echocardiographic 'reference values', their use for clinical decision-making remains questionable. AIMS: The aim of the 'Normal Reference Ranges for Echocardiography Study (NORRE Study)' is to obtain a set of 'normal values' for cardiac chamber geometry and function in a large cohort of healthy Caucasian individuals aged over a wide range of ages (25-75 years) using both conventional and advanced e…

MaleRadiology Nuclear Medicine and ImagingMESH: Echocardiography DopplerLeftSex Factor030204 cardiovascular system & hematologyDoppler echocardiographyDoppler imagingMESH: Stroke VolumeMESH: Ventricular Function LeftVentricular Function Left030218 nuclear medicine & medical imagingHeart VentricleCohort Studies0302 clinical medicine[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingNuclear Medicine and ImagingVentricular FunctionAge FactorProspective StudiesProspective cohort studyMESH: Cohort StudiesMESH: Aged2D and 3D echocardiographyMESH: Middle Agedmedicine.diagnostic_testAnthropometryAge FactorsDopplerGeneral MedicinePulsedReference Standardsreference valuesMiddle AgedCardiac mechanicEchocardiography Doppler3. Good healthEuropeMESH: Echocardiography Doppler PulsedCardiac mechanicsEchocardiographyMESH: Reference Standards[SDV.IB]Life Sciences [q-bio]/BioengineeringFemaleRadiologyCardiology and Cardiovascular Medicine[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingCohort study2D and 3D echocardiography; Cardiac mechanics; Chamber size and function; M-mode; reference values; Adult; Age Factors; Aged; Anthropometry; Cohort Studies; Echocardiography Doppler; Echocardiography Doppler Pulsed; Europe; Female; Heart Ventricles; Humans; Male; Middle Aged; Prospective Studies; Reference Standards; Sensitivity and Specificity; Sex Factors; Stroke Volume; Ventricular Function Left; Cardiology and Cardiovascular Medicine; Radiology Nuclear Medicine and ImagingHumanAdultmedicine.medical_specialtyHeart VentriclesM-modeChamber size and functionSensitivity and Specificity03 medical and health sciencesSex FactorsMESH: Sex FactorsMESH: Anthropometry[SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular systemmedicineHumansMedical physicsAgedMESH: Age FactorsEchocardiography Doppler PulsedReproducibilityMESH: Humansbusiness.industryreference valueMESH: AdultStroke VolumeMESH: MaleMESH: Prospective StudiesMESH: Sensitivity and SpecificitySurgeryClinical trialProspective StudieSample size determinationReference StandardObservational studyMESH: EuropeMESH: Heart VentriclesCohort StudiebusinessMESH: Female
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Implicit learning of a repeated segment in continuous tracking: A reappraisal

2006

Several prior studies (e.g., Shea, Wulf, Whitacre, & Park, 2001; Wulf & Schmidt, 1997) have apparently demonstrated implicit learning of a repeated segment in continuous-tracking tasks. In two conceptual replications of these studies, we failed to reproduce the original findings. However, these findings were reproduced in a third experiment, in which we used the same repeated segment as that used in the Wulf et al. studies. Analyses of the velocity and the acceleration of the target suggests that this repeated segment could be easier to track than the random segments serving as control, accounting for the results of Wulf and collaborators. Overall these experiments suggest that lea…

MaleSerial reaction timeTime Factors[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingPhysiologySpeech recognition[SHS.PSY]Humanities and Social Sciences/Psychology050109 social psychologyExperimental and Cognitive Psychology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingTracking (particle physics)050105 experimental psychologyRandom Allocation[ SHS.PSY ] Humanities and Social Sciences/PsychologyAcceleration[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingPhysiology (medical)Reaction TimeHumansLearningTraitement du signal et de l'imagePsychology0501 psychology and cognitive sciencesStudentsGeneral PsychologyAnalysis of VarianceCommunicationbusiness.industry05 social sciencesSignal and Image processingRetention PsychologyRecognition PsychologyGeneral MedicineImplicit learningNeuropsychology and Physiological PsychologyPsychologieFemalebusinessPsychology[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingPsychomotor PerformanceTraitement du signal et de l'image (Informatique)
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Common variation in PHACTR1 is associated with susceptibility to cervical artery dissection

2014

Item does not contain fulltext Cervical artery dissection (CeAD), a mural hematoma in a carotid or vertebral artery, is a major cause of ischemic stroke in young adults although relatively uncommon in the general population (incidence of 2.6/100,000 per year). Minor cervical traumas, infection, migraine and hypertension are putative risk factors, and inverse associations with obesity and hypercholesterolemia are described. No confirmed genetic susceptibility factors have been identified using candidate gene approaches. We performed genome-wide association studies (GWAS) in 1,393 CeAD cases and 14,416 controls. The rs9349379[G] allele (PHACTR1) was associated with lower CeAD risk (odds ratio…

Male[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging[SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/NeurobiologyMyocardial InfarctionGenome-wide association studyCarotid Artery Internal DissectionGastroenterologyepidemiology [Carotid Artery Internal Dissection]Brain Ischemia0302 clinical medicineMigraine DisorderOdds RatioFinlandVertebral Artery Dissection0303 health scienceseducation.field_of_studyepidemiology [Hypercholesterolemia]MESH: Middle AgedMESH: Polymorphism Single NucleotidePhactr-1 protein humanMESH: Brain IschemiaMESH: Follow-Up Studies3. Good healthMESH: Myocardial InfarctionHumanmedicine.medical_specialtyMigraine DisordersHypercholesterolemiaMESH: Vertebral Artery DissectionLower riskgenetics [Brain Ischemia]ArticleFollow-Up StudieMESH: Carotid Artery Internal Dissection03 medical and health sciencesGeneticSDG 3 - Good Health and Well-beinggenetics [Carotid Artery Internal Dissection]GeneticsGenetic predispositionepidemiology [Brain Ischemia]Humansepidemiology [Vertebral Artery Dissection]PolymorphismeducationAllelesMESH: Humansgenetics [Vertebral Artery Dissection]MESH: AdultOdds ratioMicrofilament Proteinmedicine.diseaseAdult; Brain Ischemia; Carotid Artery Internal Dissection; Female; Finland; Follow-Up Studies; Genetic Pleiotropy; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Hypercholesterolemia; Hypertension; Male; Microfilament Proteins; Middle Aged; Migraine Disorders; Myocardial Infarction; Obesity; Odds Ratio; Risk Factors; Vertebral Artery Dissection; Alleles; Polymorphism Single NucleotideMESH: Genome-Wide Association StudyCarotid ArteryMESH: Female030217 neurology & neurosurgeryepidemiology [Finland]Cervical ArteryVertebral artery dissectionepidemiology [Hypertension]MESH: HypertensionRisk FactorsMESH: Risk FactorsMESH: ObesityStrokeAlleleGeneticsDissectionMESH: FinlandMicrofilament ProteinsMESH: Genetic Predisposition to DiseaseMESH: HypercholesterolemiaGenetic PleiotropySingle NucleotideMiddle AgedMESH: Migraine DisordersDisorders of movement Donders Center for Medical Neuroscience [Radboudumc 3]epidemiology [Myocardial Infarction][INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]HypertensionFemale[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingAdultPopulationMESH: Genetic Pleiotropyphysiology [Microfilament Proteins]BiologyPolymorphism Single NucleotideMESH: Microfilament ProteinsInternal medicineddc:570medicine[INFO.INFO-IM]Computer Science [cs]/Medical ImagingGenetic Predisposition to DiseaseObesity030304 developmental biologyepidemiology [Obesity]Risk FactorMESH: Alleles[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]InternalMESH: Odds RatioMESH: Maleepidemiology [Migraine Disorders]genetics [Microfilament Proteins]Follow-Up StudiesGenome-Wide Association Study
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Computer-Aided Detection for Prostate Cancer Detection based on Multi-Parametric Magnetic Resonance Imaging

2017

International audience; Prostate cancer (CaP) is the second most diagnosed cancer in men all over the world. In the last decades, new imaging techniques based on magnetic resonance imaging (MRI) have been developed improving diagnosis. In practice, diagnosis is affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and diagnosis (CAD) systems are being designed to help radiologists in their clinical practice. We propose a CAD system taking advantage of all MRI modalities (i.e., T2-W-MRI, DCE-MRI, diffusion weighted (DW)-MRI, MRSI). The aim of this CAD system was to provide a probabilistic map of cancer…

Malemedicine.medical_specialtySource codemedia_common.quotation_subject[INFO.INFO-IM] Computer Science [cs]/Medical ImagingContrast MediaCAD[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing030218 nuclear medicine & medical imaging03 medical and health sciencesProstate cancer0302 clinical medicine[STAT.ML]Statistics [stat]/Machine Learning [stat.ML]Prostatemedicine[INFO.INFO-IM]Computer Science [cs]/Medical ImagingHumans[ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML][SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingmedia_commonMulti parametricModality (human–computer interaction)[ INFO.INFO-IM ] Computer Science [cs]/Medical Imagingmedicine.diagnostic_testbusiness.industryProstatic NeoplasmsCancerMagnetic resonance imagingmedicine.diseaseMagnetic Resonance Imaging[STAT.ML] Statistics [stat]/Machine Learning [stat.ML]3. Good healthmedicine.anatomical_structureRadiologybusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing030217 neurology & neurosurgery
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Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review

2015

Prostate cancer is the second most diagnosed cancer of men all over the world. In the last few decades, new imaging techniques based on Magnetic Resonance Imaging (MRI) have been developed to improve diagnosis. In practise, diagnosis can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and computer-aided diagnosis systems have been designed to help radiologists in their clinical practice. Research on computer-aided systems specifically focused for prostate cancer is a young technology and has been part of a dynamic field of research for the last 10years. This survey aims to provide a comprehen…

Malemedicine.medical_specialtyTime FactorsHealth InformaticsCAD[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingProstate cancerImage Processing Computer-AssistedMedicineHumansMass ScreeningMedical physicsDiagnosis Computer-AssistedObserver VariationMulti parametricmedicine.diagnostic_testbusiness.industryCarcinomaProstatic NeoplasmsReproducibility of ResultsMagnetic resonance imagingmedicine.diseaseMagnetic Resonance ImagingComputer aided detection3. Good healthComputer Science ApplicationsClinical PracticeMultiple factorsComputer-aided diagnosisResearch DesignNeural Networks ComputerNeoplasm Gradingbusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingMedical InformaticsSoftware
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