Search results for "Pattern recognition"

showing 10 items of 2301 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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An Image Segmentation Algorithm based on Community Detection

2016

International audience; With the recent advances in complex networks, image segmentation becomes one of the most appropriate application areas. In this context, we propose in this paper a new perspective of image segmentation by applying two efficient community detection algorithms. By considering regions as communities, these methods can give an over-segmented image that has many small regions. So, the proposed algorithms are improved to automatically merge those neighboring regions agglomerative to achieve the highest modularity/stability. To produce sizable regions and detect homogeneous communities, we use the combination of a feature based on the Histogram of Oriented Gradients of the …

[ INFO ] Computer Science [cs]Computer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentation02 engineering and technology[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Minimum spanning tree-based segmentationImage texture0202 electrical engineering electronic engineering information engineeringcommunity detection[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]Segmentation[INFO]Computer Science [cs][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]modularityImage segmentationSegmentation-based object categorizationbusiness.industry[ INFO.INFO-RB ] Computer Science [cs]/Robotics [cs.RO]Pattern recognitionImage segmentationcomplex networksHistogram of oriented gradientsRegion growing020201 artificial intelligence & image processingArtificial intelligencebusiness
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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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Une approche structurelle pour la reconnaissance de notices bibliographiques

1995

National audience; Cet article présente un système de reconnaissance de la structure logique de notices bibliographiques en vue de la conversion rétrospective de catalogues de bibliothèques. Le système est guidé par un modèle de structures de la classe des notices, construit sur la base de spécifications détaillées par la bibliothèque. Le modèle fait intervenir aussi bien des connaissances sur la macro-structure des notices que sur la micro-structure de leur contenu. La reconnaissance de la structure d'une notice consiste à retrouver, à partir d'un flux OCR (Optical Character Recognition), sa structure logique spécifique, conformément aux descriptions du modèle. Le résultat est un flux stru…

[ INFO.INFO-DL ] Computer Science [cs]/Digital Libraries [cs.DL]NoticeDocument structureBibliographySGMLFormat UNIMARCTechnical instructionsUNIMARC formatReconnaissance formePattern recognitionDocument analysis[INFO.INFO-DL]Computer Science [cs]/Digital Libraries [cs.DL]Analyse documentaire[INFO.INFO-DL] Computer Science [cs]/Digital Libraries [cs.DL]Structure documentBibliographie
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Automatic classification of tissues using T1 and T2 relaxation times from prostate MRI: a step toward generation of PET/MR attenuation map

2015

This paper presents a new methodology providing the first step towards generating attenuation maps for PET/MR systems based solely on MR information. The new method segments and classifies the attenuation-differing regions of the patient's pelvis based on acquired T 1 - and T 2 -weighted MR data sets and anatomical-based knowledge by computing the tissue specific T 1 and T 2 relaxation times, using a robust implementation of the weighted fuzzy C-means algorithm and applying a novel process to detect bones. We have demonstrated the feasibility of this approach by correctly segmenting and classifying six differing regions of structural and anatomical importance: fat, muscle, prostate, air, ba…

[ INFO.INFO-IM ] Computer Science [cs]/Medical ImagingComputer sciencebusiness.industryAttenuation[INFO.INFO-IM] Computer Science [cs]/Medical ImagingRelaxation (iterative method)Pattern recognition030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicinemedicine.anatomical_structure[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV]ProstateT2 relaxation[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV][ INFO.INFO-TI ] Computer Science [cs]/Image Processingmedicine[INFO.INFO-IM]Computer Science [cs]/Medical ImagingArtificial intelligencebusiness030217 neurology & neurosurgeryComputingMilieux_MISCELLANEOUS
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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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