Search results for "Computer Vision and Pattern Recognition"

showing 10 items of 997 documents

Space–bandwidth product of optical signals and systems

1996

The space–bandwidth product (SW) is fundamental for judging the performance of an optical system. Often the SW of a system is defined only as a pure number that counts the degrees of freedom of the system. We claim that a quasi-geometrical representation of the SW in the Wigner domain is more useful. We also represent the input signal as a SW in the Wigner domain. For perfect signal processing it is necessary that the system SW fully embrace the signal SW.

Signal processingbusiness.industryComputer scienceBandwidth (signal processing)TopologyAtomic and Molecular Physics and OpticsElectronic Optical and Magnetic Materialssymbols.namesakeFourier transformOpticssymbolsComputer Vision and Pattern RecognitionSpatial frequencybusinessJournal of the Optical Society of America A
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The Large Area Detector onboard the eXTP mission

2018

The eXTP (enhanced X-ray Timing and Polarimetry) mission is a major project of the Chinese Academy of Sciences (CAS) and China National Space Administration (CNSA) currently performing an extended phase A study and proposed for a launch by 2025 in a low-earth orbit. The eXTP scientific payload envisages a suite of instruments (Spectroscopy Focusing Array, Polarimetry Focusing Array, Large Area Detector and Wide Field Monitor) offering unprecedented simultaneous wide-band X-ray spectral, timing and polarimetry sensitivity. A large European consortium is contributing to the eXTP study and it is expected to provide key hardware elements, including a Large Area Detector (LAD). The LAD instrumen…

Silicon detectorX-ray AstronomyComputer sciencecapillary platePolarimetryFOS: Physical sciencesField of viewContext (language use)Condensed Matter Physic01 natural sciencesSettore FIS/05 - Astronomia E Astrofisica0103 physical sciencesElectroniccapillary plates; Silicon detectors; Timing; X-ray Astronomy; Electronic Optical and Magnetic Materials; Condensed Matter Physics; Computer Science Applications1707 Computer Vision and Pattern Recognition; Applied Mathematics; Electrical and Electronic EngineeringTimingOptical and Magnetic MaterialsAerospace engineeringSpectral resolutionElectrical and Electronic Engineering010306 general physicscapillary plates; Silicon detectors; Timing; X-ray Astronomy; astro-ph.IM; astro-ph.IM; Electronic Optical and Magnetic Materials; Condensed Matter Physics; Computer Science Applications1707 Computer Vision and Pattern Recognition; Applied Mathematics; Electrical and Electronic EngineeringInstrumentation and Methods for Astrophysics (astro-ph.IM)X-ray astronomycapillary plates010308 nuclear & particles physicsbusiness.industryPayloadElectronic Optical and Magnetic MaterialApplied MathematicsDetectorAntenna apertureComputer Science Applications1707 Computer Vision and Pattern RecognitionCondensed Matter PhysicsApplied MathematicSilicon detectorsAstrophysics - Instrumentation and Methods for Astrophysicsbusinessastro-ph.IM
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Energy balance in single exposure multispectral sensors

2013

International audience; Recent simulations of multispectral sensors are based on a simple Gaussian model, which includes filters transmittance and substrate absorption. In this paper we want to make the distinction between these two layers. We discuss the balance of energy by channel in multispectral solid state sensors and propose an updated simple Gaussian model to simulate multispectral sensors. Results are based on simulation of typical sensor configurations.

SiliconMaterials science[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processingoptical sensorsChannel (digital image)Equations[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingMultispectral imageComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONPhotodetectorGaussian processes02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing01 natural sciences010309 opticssymbols.namesakeMathematical model[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing0103 physical sciences0202 electrical engineering electronic engineering information engineeringTransmittanceComputer Science::Networking and Internet ArchitectureSpectral and color filter arraysoptical filtersOptical filterGaussian processPhysics::Atmospheric and Oceanic Physics[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingRemote sensingtransmittance filterSubstratesSensorsGaussian modelmultispectral solid state sensorCamerasenergy balancespectral analysisConvolutionexposure multispectral sensorComputer Science::Computer Vision and Pattern Recognitionsubstrate absorptionlight absorptionlight sensorsymbolstransmittance filters020201 artificial intelligence & image processingGaussian network model[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingEnergy (signal processing)
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An enhanced random walk algorithm for delineation of head and neck cancers in PET studies

2017

An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesions with bifurcations. In addition, an adaptive probability threshold and a k-means based clustering technique have been integrated in the proposed enhanced RW algorithm. The new threshold is capable of following the intensity changes between adjacent slices along the whole cancer volume, leading to an operator-independent algorithm. Validation experiments were first conducted on phantom studies:…

Similarity (geometry)Computer sciencePET imagingBiomedical EngineeringRandom walk030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicinemedicineImage Processing Computer-AssistedHumansSegmentationComputer visionCluster analysisEvent (probability theory)Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionimedicine.diagnostic_testbusiness.industryPhantoms ImagingBiological target volume; Head and neck cancer segmentation; PET imaging; Random walksComputer Science ApplicationPattern recognitionRandom walkComputer Science ApplicationsBiological target volumeHausdorff distancePositron emission tomographyHead and Neck Neoplasms030220 oncology & carcinogenesisPositron-Emission TomographyArtificial intelligenceHead and neck cancer segmentationComputer Vision and Pattern RecognitionbusinessAlgorithmsBiological target volume Head and neck cancer segmentation PET imaging Random walks Algorithms Head and Neck Neoplasms Humans Image Processing Computer-Assisted Phantoms Imaging Positron-Emission TomographyVolume (compression)
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A mutual GrabCut method to solve co-segmentation

2013

Publised version of an article from the journal:Eurasip Journal on Image and Video Processing. Also available on SpringerLink:http://dx.doi.org/10.1186/1687-5281-2013-20. Open Access Co-segmentation aims at segmenting common objects from a group of images. Markov random field (MRF) has been widely used to solve co-segmentation, which introduces a global constraint to make the foreground similar to each other. However, it is difficult to minimize the new model. In this paper, we propose a new Markov random field-based co-segmentation model to solve co-segmentation problem without minimization problem. In our model, foreground similarity constraint is added into the unary term of MRF model ra…

Similarity (geometry)Markov random fieldComputer sciencebusiness.industryVDP::Mathematics and natural science: 400::Information and communication science: 420::Algorithms and computability theory: 422ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONVDP::Technology: 500::Information and communication technology: 550Pattern recognitionFunction (mathematics)Term (time)Constraint (information theory)GrabCutComputer Science::Computer Vision and Pattern RecognitionCutSignal ProcessingSegmentationArtificial intelligenceElectrical and Electronic EngineeringbusinessInformation SystemsEURASIP Journal on Image and Video Processing
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Trademarks recognition based on local regions similarities

2010

This paper deals with content based image retrieval. We propose a logo recognition algorithm based on local regions, where the trademark (or logo) image is segmented by the clustering of points of interest obtained by Harris corners detector. The minimum rectangle surrounding each cluster is detected forming the regions of interest. Global features such as Hu moments and histograms of each local region are combined to find similar logos in the database. Similarity is measured based on the integrated minimum average distance of the individual components. The results obtained demonstrate tolerance to logos distortions such as rotation, occlusion and noise.

Similarity (geometry)business.industryComputer scienceMathematics::History and OverviewComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONCorner detectionPattern recognitionImage segmentationContent-based image retrievalEdge detectionComputingMethodologies_PATTERNRECOGNITIONComputer Science::Computer Vision and Pattern RecognitionPattern recognition (psychology)Computer visionArtificial intelligencebusinessCluster analysisImage retrieval10th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2010)
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The Cryogenic AntiCoincidence detector for ATHENA: the progress towards the final pixel design

2014

“The Hot and Energetic Universe” is the scientific theme approved by the ESA SPC for a Large mission to be flown in the next ESA slot (2028th) timeframe. ATHENA is a space mission proposal tailored on this scientific theme. It will be the first X-ray mission able to perform the so-called “Integral field spectroscopy”, by coupling a high-resolution spectrometer, the X-ray Integral Field Unit (X-IFU), to a high performance optics so providing detailed images of its field of view (5’ in diameter) with an angular resolution of 5” and fine energy-spectra (2.5eV@E<7keV). The X-IFU is a kilo-pixel array based on TES (Transition Edge Sensor) microcalorimeters providing high resolution spectroscopy …

SimulationsSiliconWarm–hot intergalactic mediumField of viewOrbital mechanicsOpticsField spectroscopyGalactic astronomyX-raysElectronicAngular resolutionOptical and Magnetic MaterialsElectrical and Electronic EngineeringAnticoincidenceImage resolutionSpectroscopyPhysicsSpatial resolutionEquipment and servicesSpectrometerSpectrometersbusiness.industrySensorsApplied MathematicsDetectorComputer Science Applications1707 Computer Vision and Pattern RecognitionCondensed Matter PhysicsATHENAAnticoincidence; ATHENA; Cryogenic detectors; TES; Electronic Optical and Magnetic Materials; Condensed Matter Physics; Computer Science Applications1707 Computer Vision and Pattern Recognition; Applied Mathematics; Electrical and Electronic EngineeringCryogenic detectorsTransition edge sensorbusinessTES
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An efficient prototype merging strategy for the condensed 1-NN rule through class-conditional hierarchical clustering

2002

Abstract A generalized prototype-based classification scheme founded on hierarchical clustering is proposed. The basic idea is to obtain a condensed 1-NN classification rule by merging the two same-class nearest clusters, provided that the set of cluster representatives correctly classifies all the original points. Apart from the quality of the obtained sets and its flexibility which comes from the fact that different intercluster measures and criteria can be used, the proposed scheme includes a very efficient four-stage procedure which conveniently exploits geometric cluster properties to decide about each possible merge. Empirical results demonstrate the merits of the proposed algorithm t…

Single-linkage clusteringcomputer.software_genreComplete-linkage clusteringHierarchical clusteringk-nearest neighbors algorithmArtificial IntelligenceNearest-neighbor chain algorithmClassification ruleSignal ProcessingCluster (physics)Computer Vision and Pattern RecognitionData miningMerge (version control)computerSoftwareMathematicsPattern Recognition
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3D objects descriptors methods: Overview and trends

2017

International audience; Object recognition or object's category recognition under varying conditions is one of the most astonishing capabilities of human visual system. The scientists in computer vision have been trying for decades to reproduce this ability by implementing algorithms and providing computers with appropriate tools. Hence, several intelligent systems have been proposed. To act in this field, numerous approaches have been proposed. In this paper we present an overview of the current trend in 3D objects recognition and describe some representative state of the art methods, highlighting their limits and complexity.

Sketch recognitionComputer science3D single-object recognition[INFO.INFO-GR] Computer Science [cs]/Graphics [cs.GR]02 engineering and technology[INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG]Field (computer science)object recognitionhuman visual systemcomputer vision[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingHuman–computer interactionobject category recognition0202 electrical engineering electronic engineering information engineeringskeletonComputer vision3D objects descriptors methodsVisualization3D objects recognitionintelligent systemsNon-Controlled Indexingbusiness.industryCognitive neuroscience of visual object recognitionIntelligent decision support system[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Shape020207 software engineeringComputational modelingObject (computer science)Keypoints3D objects[INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR]VisualizationRecognition[INFO.INFO-CG] Computer Science [cs]/Computational Geometry [cs.CG]Human visual system modelSolid modelingThree-dimensional displays020201 artificial intelligence & image processingArtificial intelligencebusiness
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UBFC-Phys: A Multimodal Database For Psychophysiological Studies of Social Stress

2021

As humans, we experience social stress in countless everyday-life situations. Giving a speech in front of an audience, passing a job interview, and similar experiences all lead us to go through stress states that impact both our psychological and physiological states. Therefore, studying the link between stress and physiological responses had become a critical societal issue, and recently, research in this field has grown in popularity. However, publicly available datasets have limitations. In this article, we propose a new dataset, UBFC-Phys, collected with and without contact from participants living social stress situations. A wristband was used to measure contact blood volume pulse (BVP…

Social stressFacial expressionModalitiesComputer scienceSpeech recognition010401 analytical chemistryFeature extraction[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]020206 networking & telecommunications02 engineering and technology01 natural sciencesField (computer science)0104 chemical sciencesHuman-Computer InteractionPsychophysiology[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingStress (linguistics)0202 electrical engineering electronic engineering information engineeringTask analysisComputingMilieux_MISCELLANEOUSSoftwareIEEE Transactions on Affective Computing
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