Search results for "Pattern Recognition"

showing 10 items of 2301 documents

Does visual letter similarity modulate masked form priming in young readers of Arabic?

2018

Available online 19 January 2018 Supplementary data associated with this article can be found, in the online version, at https://doi. org/10.1016/j.jecp.2017.12.004. Supplementary data associated with this article can be found, in the online version, at https://doi. org/10.1016/j.jecp.2017.12.004. We carried out a masked priming lexical decision experiment to study whether visual letter similarity plays a role during the initial phases of word processing in young readers of Arabic (fifth graders). Arabic is ideally suited to test these effects because most Arabic letters share their basic shape with at least one other letter and differ only in the number/position of diacritical points (e.g.…

MaleRoot (linguistics)Lexical decisionWord processingDecision MakingExperimental and Cognitive Psychology050105 experimental psychology03 medical and health sciences0302 clinical medicineSimilarity (psychology)Repetition PrimingDevelopmental and Educational PsychologyLexical decision taskHumans0501 psychology and cognitive sciencesChildVisual similarityLanguage05 social sciencesDeveloping readersSemitic languagesLinguisticsPattern Recognition VisualReadingWord recognitionLexical accessMasked primingPsychologyPriming (psychology)030217 neurology & neurosurgeryWord (group theory)
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Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge

2014

Contains fulltext : 137969.pdf (Publisher’s version ) (Open Access) Prostate MRI image segmentation has been an area of intense research due to the increased use of MRI as a modality for the clinical workup of prostate cancer. Segmentation is useful for various tasks, e.g. to accurately localize prostate boundaries for radiotherapy or to initialize multi-modal registration algorithms. In the past, it has been difficult for research groups to evaluate prostate segmentation algorithms on multi-center, multi-vendor and multi-protocol data. Especially because we are dealing with MR images, image appearance, resolution and the presence of artifacts are affected by differences in scanners and/or …

MaleScannerObserver (quantum physics)Computer scienceHealth InformaticsSensitivity and SpecificityArticleProstate cancerSegmentationImaging Three-DimensionalRobustness (computer science)Image Interpretation Computer-AssistedmedicineHumansRadiology Nuclear Medicine and imagingSegmentationChallengeProtocol (science)Modality (human–computer interaction)Radiological and Ultrasound TechnologyProstateProstatic NeoplasmsReproducibility of ResultsReference Standardsmedicine.diseaseImage EnhancementComputer Graphics and Computer-Aided DesignMagnetic Resonance ImagingActive appearance modelUrological cancers Radboud Institute for Health Sciences [Radboudumc 15]Computer Vision and Pattern RecognitionArtifactsAlgorithmAlgorithmsRare cancers Radboud Institute for Health Sciences [Radboudumc 9]MRI
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Evaluation of Deep Neural Networks for Semantic Segmentation of Prostate in T2W MRI

2020

In this paper, we present an evaluation of four encoder&ndash

MaleSimilarity (geometry)Computer scienceSegNet02 engineering and technologylcsh:Chemical technologyBiochemistryArticleencoder–decoder030218 nuclear medicine & medical imagingAnalytical Chemistry03 medical and health sciencesProstate cancer0302 clinical medicineProstateImage Processing Computer-Assisted0202 electrical engineering electronic engineering information engineeringmedicineHumanslcsh:TP1-1185SegmentationElectrical and Electronic EngineeringInstrumentationmedicine.diagnostic_testPixelbusiness.industryProstateCNNsPattern recognitionMagnetic resonance imagingFCNmedicine.diseaseMagnetic Resonance ImagingU-NetAtomic and Molecular Physics and OpticsSemanticsIntensity normalizationmedicine.anatomical_structureDeepLabV3+Deep neural networks020201 artificial intelligence & image processingNeural Networks ComputerArtificial intelligencebusinessDNNSensors
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Deep Learning Network for Segmentation of the Prostate Gland With Median Lobe Enlargement in T2-weighted MR Images: Comparison With Manual Segmentati…

2021

Purpose: Aim of this study was to evaluate a fully automated deep learning network named Efficient Neural Network (ENet) for segmentation of prostate gland with median lobe enlargement compared to manual segmentation. Materials and Methods: One-hundred-three patients with median lobe enlargement on prostate MRI were retrospectively included. Ellipsoid formula, manual segmentation and automatic segmentation were used for prostate volume estimation using T2 weighted MRI images. ENet was used for automatic segmentation; it is a deep learning network developed for fast inference and high accuracy in augmented reality and automotive scenarios. Student t-test was performed to compare prostate vol…

MaleSimilarity (network science)ProstateImage Processing Computer-AssistedmedicineHumansRadiology Nuclear Medicine and imagingSegmentationRetrospective StudiesprostateArtificial neural networkbusiness.industryDeep learningProstate MRIENetsegmentationPattern recognitionDeep learningMagnetic Resonance ImagingEllipsoidLobemedicine.anatomical_structuredeep learning networkNeural Networks ComputerArtificial intelligencebusinessSettore MED/36 - Diagnostica Per Immagini E RadioterapiaVolume (compression)
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Spectro-Temporal Weighting of Loudness

2012

Real-world sounds like speech or traffic noise typically exhibit spectro-temporal variability because the energy in different spectral regions evolves differently as a sound unfolds in time. However, it is currently not well understood how the energy in different spectral and temporal portions contributes to loudness. This study investigated how listeners weight different temporal and spectral components of a sound when judging its overall loudness. Spectral weights were measured for the combination of three loudness-matched narrowband noises with different center frequencies. To measure temporal weights, 1,020-ms stimuli were presented, which randomly changed in level every 100 ms. Tempora…

MaleSound SpectrographyTime FactorsLoudness Perceptionlcsh:MedicineSocial and Behavioral SciencesLoudnessNarrowbandPsychologylcsh:ScienceSound pressureMathematicsMultidisciplinaryPhysicsClassical MechanicsExperimental PsychologySensory SystemsSoundmedicine.anatomical_structureAuditory SystemAuditory PerceptionRegression AnalysisFemaleSensory PerceptionPsychoacousticsResearch ArticleAdultContext (language use)Sensitivity and SpecificityYoung AdultPsychophysicsmedicineHumansAuditory systemPsychoacousticsBiologyBehaviorModels Statisticalbusiness.industrylcsh:RPattern recognitionAcousticsWeightingNoiseAcoustic StimulationROC Curvelcsh:QArtificial intelligenceNoiseAttention (Behavior)businessNeurosciencePLoS ONE
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The effect of body posture on long range time-to-contact estimation

2011

On Earth, gravity accelerates freely moving objects downward, whereas upward-moving objects are being decelerated. Do humans take internalised knowledge of gravity into account when estimating time-to-contact (TTC, the time remaining before the moving object reaches the observer)? To answer this question, we created a motion-prediction task in which participants saw the initial part of an object's trajectory moving on a collision course prior to an occlusion. Observers had to judge when the object would make contact with them. The visual scene was presented with a head-mounted display. Participants lay either supine (looking up) or prone (looking down), suggestive of the ball either rising…

MaleSupine positionComputer scienceMotion PerceptionTime to contactExperimental and Cognitive Psychology050105 experimental psychology03 medical and health sciencesJudgmentUser-Computer InterfaceYoung Adult0302 clinical medicineArtificial IntelligenceOrientationImmediacyOcclusionProne PositionSupine PositionHumans0501 psychology and cognitive sciencesComputer visionCommunicationDepth Perception[SDV.NEU.PC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Psychology and behaviorbusiness.industryBody posture05 social sciencesCOMPORTEMENT DU CONDUCTEURObserver (special relativity)CollisionSensory SystemsOphthalmologyPattern Recognition VisualTime PerceptionFemaleArtificial intelligencebusinessPerceptual Masking030217 neurology & neurosurgeryGravitation
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Automatic classification of tissues on pelvic MRI based on relaxation times and support vector machine

2019

International audience; Tissue segmentation and classification in MRI is a challenging task due to a lack of signal intensity standardization. MRI signal is dependent on the acquisition protocol, the coil profile, the scanner type, etc. While we can compute quantitative physical tissue properties independent of the hardware and the sequence parameters, it is still difficult to leverage these physical properties to segment and classify pelvic tissues. The proposed method integrates quantitative MRI values (T1 and T2 relaxation times and pure synthetic weighted images) and machine learning (Support Vector Machine (SVM)) to segment and classify tissues in the pelvic region, i.e.: fat, muscle, …

MaleSupport Vector MachinePhysiologyComputer scienceBiochemistryDiagnostic Radiology030218 nuclear medicine & medical imagingFatsMachine Learning0302 clinical medicineBone MarrowProstateImmune PhysiologyRelaxation TimeMedicine and Health SciencesImage Processing Computer-AssistedSegmentationProspective StudiesMultidisciplinarymedicine.diagnostic_testPhysicsRadiology and ImagingQRelaxation (NMR)RMagnetic Resonance ImagingLipidsmedicine.anatomical_structurePhysical SciencesMedicineAnatomyResearch ArticleAdultComputer and Information SciencesImaging TechniquesScienceBladderImmunologyImage processingResearch and Analysis MethodsPelvis03 medical and health sciencesExocrine GlandsDiagnostic MedicineArtificial IntelligenceSupport Vector Machinesmedicine[INFO.INFO-IM]Computer Science [cs]/Medical ImagingHumansRelaxation (Physics)PelvisPelvic MRIbusiness.industryBiology and Life SciencesMagnetic resonance imagingPattern recognitionRenal SystemSupport vector machineImmune SystemSpin echoProstate GlandArtificial intelligenceBone marrowbusiness030217 neurology & neurosurgery
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A co-registration investigation of inter-word spacing and parafoveal preview: Eye movements and fixation-related potentials

2019

Participants’ eye movements (EMs) and EEG signal were simultaneously recorded to examine foveal and parafoveal processing during sentence reading. All the words in the sentence were manipulated for inter-word spacing (intact spaces vs. spaces replaced by a random letter) and parafoveal preview (identical preview vs. random letter string preview). We observed disruption for unspaced text and invalid preview conditions in both EMs and fixation-related potentials (FRPs). Unspaced and invalid preview conditions received longer reading times than spaced and valid preview conditions. In addition, the FRP data showed that unspaced previews disrupted reading in earlier time windows of analysis, com…

MaleTime FactorsEye MovementsPhysiologyVisual SystemVisionComputer scienceSpeech recognitionSensory PhysiologyVisual PhysiologySocial ScienceslukeminensilmänliikkeetOcular physiology0302 clinical medicineFovealMedicine and Health SciencesPsychologyAttentionMacula LuteaEEGNeurolinguisticsClinical NeurophysiologyBrain MappingMultidisciplinaryQ05 social sciencesRElectroencephalographyHealthy VolunteersSensory SystemsSemanticsElectrophysiologyBioassays and Physiological AnalysisPattern Recognition VisualBrain ElectrophysiologyPhysical SciencestekstinymmärtäminenMedicineFemaleSensory PerceptionAnatomyResearch ArticleAdultAdolescentImaging TechniquesPermutationScienceNeurophysiologyCo registrationNeuroimagingFixation OcularResearch and Analysis Methods050105 experimental psychologyYoung Adult03 medical and health sciencesHumans0501 psychology and cognitive sciencesScalpDiscrete MathematicsElectrophysiological TechniquesCognitive PsychologyBiology and Life SciencesEye movementLinguisticsReadingSentence ProcessingCombinatoricsFixation (visual)katseenseurantaCognitive ScienceClinical MedicineHeadMathematics030217 neurology & neurosurgeryNeurosciencePLOS ONE
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TMS-evoked long-lasting artefacts: A new adaptive algorithm for EEG signal correction

2017

Abstract Objective During EEG the discharge of TMS generates a long-lasting decay artefact (DA) that makes the analysis of TMS-evoked potentials (TEPs) difficult. Our aim was twofold: (1) to describe how the DA affects the recorded EEG and (2) to develop a new adaptive detrend algorithm (ADA) able to correct the DA. Methods We performed two experiments testing 50 healthy volunteers. In experiment 1, we tested the efficacy of ADA by comparing it with two commonly-used independent component analysis (ICA) algorithms. In experiment 2, we further investigated the efficiency of ADA and the impact of the DA evoked from TMS over frontal, motor and parietal areas. Results Our results demonstrated t…

MaleTime Factorsmedicine.medical_treatmentElectroencephalographySignal0302 clinical medicineSignal correctionDetrendEEGAdaptive algorithmmedicine.diagnostic_test05 social sciencesElectroencephalographyTranscranial Magnetic StimulationSensory SystemsAlgorithmNeurologyArtefact; Detrend; EEG; ICA; TMS; Sensory Systems; Neurology; Neurology (clinical); Physiology (medical)ArtifactFemalePrimary motor cortexArtifactsPsychologyAlgorithmsHumanAdultTime Factor050105 experimental psychologyNOYoung Adult03 medical and health sciencesPhysiology (medical)medicineHumansMiddle frontal gyrus0501 psychology and cognitive sciencesICAArtefactSettore M-PSI/02 - Psicobiologia E Psicologia Fisiologicabusiness.industryPattern recognitionIndependent component analysisTranscranial magnetic stimulationTMSNeurology (clinical)Artificial intelligenceSensory SystembusinessNeuroscience030217 neurology & neurosurgeryClinical Neurophysiology
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Across space and time : infants learn from backward and forward visual statistics

2016

International audience; This study investigates whether infants are sensitive to backward and forward transitional probabilities within temporal and spatial visual streams. Two groups of 8-month-old infants were familiarized with an artificial grammar of shapes, comprising backward and forward base pairs (i.e. two shapes linked by strong backward or forward transitional probability) and part-pairs (i.e. two shapes with weak transitional probabilities in both directions). One group viewed the continuous visual stream as a temporal sequence, while the other group viewed the same stream as a spatial array. Following familiarization, infants looked longer at test trials containing part-pairs th…

MaleTime perspectiveTime FactorsVisual perceptionspatial visual streamsCognitive NeuroscienceSpatial abilityVisual input[SHS.PSY]Humanities and Social Sciences/Psychology[ SCCO.PSYC ] Cognitive science/PsychologyArticle050105 experimental psychologypsyc[ SHS.PSY ] Humanities and Social Sciences/PsychologyChild DevelopmentDevelopmental and Educational PsychologyHumansLearning0501 psychology and cognitive sciences10. No inequalityProbabilityAnalysis of VarianceSequenceCommunicationSpacetimeStatistical learningbusiness.industryExtramural05 social sciencesAge FactorsInfantPattern recognitionsensitivityTemporal sequencesSpace PerceptionTime Perception[SCCO.PSYC]Cognitive science/PsychologyVisual PerceptionFemaleArtificial intelligencePsychologybusinessPhotic Stimulation050104 developmental & child psychology
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