Search results for " Pattern Recognition"

showing 10 items of 1050 documents

Quantitative comparison of new image processing methods for volumetric analysis of left ventricular contrast echocardiograms

2003

An effort has been made to develop image processing methods which allow a definite and precise tracking of the borderline of the ventricle in two-dimensional echocardiograms. Experience is reported with two new methods, which are based on the gray-level rise (GL) and the signal-to-noise ratio (SNR) in combined heart-phase-triggered image series. A quantitative comparison of these time-series methods is presented with respect to the interpretation of a single native image (noncontrast image), a single contrast-material image, the corresponding subtraction image, and the corresponding color superposition image. The comparison is based on the calculation of the ejection fraction using the abov…

Image SeriesComputer sciencebusiness.industrymedia_common.quotation_subjectSubtractionImage processingSuperposition principleSignal-to-noise ratio (imaging)Computer Science::Computer Vision and Pattern RecognitionMedical imagingContrast (vision)Computer visionArtificial intelligencebusinessImage resolutionmedia_common[1989] Proceedings. Computers in Cardiology
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The role of perceptual contrast non-linearities in image transform quantization

2000

Abstract The conventional quantizer design based on average error minimization over a training set does not guarantee a good subjective behavior on individual images even if perceptual metrics are used. In this work a novel criterion for transform coder design is analyzed in depth. Its aim is to bound the perceptual distortion in each individual quantization according to a non-linear model of early human vision. A common comparison framework is presented to describe the qualitative behavior of the optimal quantizers under the proposed criterion and the conventional rate-distortion based criterion. Several underlying metrics, with and without perceptual non-linearities, are used with both cr…

Image codingTraining setbusiness.industryQuantization (signal processing)media_common.quotation_subjectComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONPerceptionSignal ProcessingComputer visionComputer Vision and Pattern RecognitionArtificial intelligencePerceptual DistortionMinificationbusinessAlgorithmMathematicsmedia_commonImage and Vision Computing
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Analysis of image formation with a photon scanning tunneling microscope

1996

International audience; The photon scanning tunneling microscope (PSTM) is based on the frustration of a total internal reflected beam by the end of an optical fiber. Until now it has been used to obtain topographic information, generally for smooth samples. We report theoretical as well as experimental results on the observation of a step on a quartz substrate with the PSTM. These results demonstrate the effects on image formation of the distance between the fiber tip and the sample surface, the orientation of the incident beam with respect to the step, the polarization, and the coherence of the light. Good agreement exists between numerical simulations and experiments. We show that a pert…

Image formation[PHYS.PHYS.PHYS-OPTICS] Physics [physics]/Physics [physics]/Optics [physics.optics]Optical fiberScanning tunneling spectroscopy02 engineering and technology01 natural scienceslaw.invention010309 opticsScanning probe microscopyOpticslaw0103 physical sciencesLight beamPhysicsTotal internal reflection[PHYS.PHYS.PHYS-OPTICS]Physics [physics]/Physics [physics]/Optics [physics.optics][ PHYS.PHYS.PHYS-OPTICS ] Physics [physics]/Physics [physics]/Optics [physics.optics]business.industryNear-field optics021001 nanoscience & nanotechnologyAtomic and Molecular Physics and OpticsElectronic Optical and Magnetic MaterialsComputer Vision and Pattern RecognitionScanning tunneling microscopebusiness0210 nano-technology
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Multimodal biometric recognition systems using deep learning based on the finger vein and finger knuckle print fusion

2020

Recognition systems using multimodal biometrics attracts attention because they improve recognition efficiency and high-security level compared to the unimodal biometrics system. In this study, the authors present a secure multimodal biometrics recognition system based on the deep learning method that uses convolutional neural networks (CNNs). The authors propose two multimodal architectures using the finger knuckle print (FKP) and the finger vein (FV) biometrics with different levels of fusion: the features level fusion and scores level fusion. The features extraction for FKP and FV are performed using transfer learning CNN architectures: AlexNet, VGG16, and ResNet50. The key step aims to …

Image fusionBiometricsbusiness.industryComputer scienceDeep learningFeature extractionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONWord error rate020206 networking & telecommunicationsPattern recognition02 engineering and technologyConvolutional neural networkSupport vector machineSignal ProcessingSoftmax function0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligenceElectrical and Electronic EngineeringbusinessSoftwareIET Image Processing
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Multi-resolution spatial unmixing for MERIS and Landsat image fusion

2016

Nowadays, the increasing quantity of applications using images from Earth Observation satellites makes demanding better spatial, spectral and temporal resolutions. Nevertheless, due to the technical constraint of a trade off between spatial and spectral resolutions, and between spatial resolution and coverage, high spatial resolution is related with low spectral and temporal resolutions and vice versa. Data fusion methods are a good solution to combine information from multiple sensors in order to obtain image products with better characteristics. In this paper, we propose an image fusion approach based on a multi-resolution and multi-source unmixing. The proposed methodology yields a compo…

Image fusionGeographic information systemPixelComputer sciencebusiness.industryResolution (electron density)ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONSensor fusionComposite image filterComputer Science::Computer Vision and Pattern RecognitionbusinessImage resolutionRemote sensingDownscaling2010 IEEE International Geoscience and Remote Sensing Symposium
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An interpolation-based data fusion scheme for enhancing the resolution of thermal image sequences

2014

In several human activities, such as agriculture and forest management, the monitoring of radiometric surface temperature is key. In particular both high spatial resolution and high acquisition rate are desirable but, due to the hardware limitations, these two characteristics are not met by the same sensor. The fusion of remotely sensed data acquired by sensors with different spatial and temporal resolution is a profitable choice to face this issue. When the real-time requirement is relaxed, the data sequence can be processed as a whole, allowing to improve the final result. Within this framework, we propose a novel batch sharpening strategy, relying on interpolation, data fusion and Bayesi…

Image fusionIrrigation ManagementSettore ING-INF/03 - TelecomunicazioniComputer sciencebusiness.industrySettore ICAR/02 - Costruzioni Idrauliche E Marittime E IdrologiaComputer Science Applications1707 Computer Vision and Pattern RecognitionSharpeningSensor fusionBayesian SmoothingThermal SharpeningMultitemporal AnalysiTemporal resolutionFace (geometry)Key (cryptography)Settore AGR/08 - Idraulica Agraria E Sistemazioni Idraulico-ForestaliComputer visionArtificial intelligencebusinessMultisensor Data FusionEarth and Planetary Sciences (all)Settore ICAR/06 - Topografia E CartografiaSub-pixel resolutionInterpolation2014 IEEE Geoscience and Remote Sensing Symposium
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Improved multi-resolution image fusion

2005

This work describes an automatic technique able to fuse different images of the same scene, acquired at different settings, in order to obtain an enhanced single representation of the scene of interest by an improved picture fusion scheme. This allows the extending of the functionalities (depth of field, dynamic range) of medium and low cost digital cameras. When multi-scale decomposition is used on multi-focused images, magnification effects of the lens focusing system cause an incorrect estimation of all pixels in the final image. In our approach new techniques able to reduce these artifacts are introduced. The algorithm has been applied both on full RGB and on color filter array (CFA) im…

Image fusionPixelbusiness.industryComputer scienceimage scenes differenceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONSensor fusionlaw.inventionLens (optics)lawComputer Science::Computer Vision and Pattern RecognitionRGB color modelColor filter arrayComputer visionDepth of fieldArtificial intelligencebusinessImage resolution2005 Digest of Technical Papers. International Conference on Consumer Electronics, 2005. ICCE.
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Ghost Detection and Removal for High Dynamic Range Images: Recent Advances

2012

23 pages; International audience; High dynamic range (HDR) image generation and display technologies are becoming increasingly popular in various applications. A standard and commonly used approach to obtain an HDR image is the multiple exposures fusion technique which consists of combining multiple images of the same scene with varying exposure times. However, if the scene is not static during the sequence acquisition, moving objects manifest themselves as ghosting artefacts in the final HDR image. Detecting and removing ghosting artefacts is an important issue for automatically generating HDR images of dynamic scenes. The aim of this paper is to provide an up-to-date review of the recentl…

Image generationExposures fusionComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technology[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]GeneralLiterature_MISCELLANEOUSImage (mathematics)Ghost detectionComputer graphics (images)0202 electrical engineering electronic engineering information engineeringComputer visionElectrical and Electronic EngineeringGhostingHigh dynamic rangeComputingMethodologies_COMPUTERGRAPHICSSequencebusiness.industry[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]020207 software engineeringHigh dynamic range imagesGhost removalSignal Processing020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligencebusinessSoftware
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Multifacet structure of observed reconstructed integral images.

2005

Three-dimensional images generated by an integral imaging system suffer from degradations in the form of grid of multiple facets. This multifacet structure breaks the continuity of the observed image and therefore reduces its visual quality. We perform an analysis of this effect and present the guidelines in the design of lenslet imaging parameters for optimization of viewing conditions with respect to the multifacet degradation. We consider the optimization of the system in terms of field of view, observer position and pupil function, lenslet parameters, and type of reconstruction. Numerical tests are presented to verify the theoretical analysis.

Image qualityComputer scienceInformation Storage and RetrievalField of viewIterative reconstructionLensletModels BiologicalSensitivity and SpecificityUser-Computer InterfaceOpticsImaging Three-DimensionalArtificial IntelligencePupil functionImage Interpretation Computer-AssistedComputer GraphicsHumansComputer SimulationIntegral imagingModels Statisticalbusiness.industryReproducibility of ResultsObserver (special relativity)GridImage EnhancementAtomic and Molecular Physics and OpticsElectronic Optical and Magnetic MaterialsVisual PerceptionComputer Vision and Pattern RecognitionbusinessAlgorithmsJournal of the Optical Society of America. A, Optics, image science, and vision
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Roadmap on 3D integral imaging: Sensing, processing, and display

2020

This Roadmap article on three-dimensional integral imaging provides an overview of some of the research activities in the field of integral imaging. The article discusses various aspects of the field including sensing of 3D scenes, processing of captured information, and 3D display and visualization of information. The paper consists of a series of 15 sections from the experts presenting various aspects of the field on sensing, processing, displays, augmented reality, microscopy, object recognition, and other applications. Each section represents the vision of its author to describe the progress, potential, vision, and challenging issues in this field.

Image qualityComputer scienceReconeixement òptic de formesComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage processing02 engineering and technologySeguretat informàticaStereo display01 natural sciencesXifratge (Informàtica)Field (computer science)010309 opticsOpticsHuman–computer interactionComputer security0103 physical sciencesMicroscopyComunicacions òptiquesData encryption (Computer science)Integral imagingOptical pattern recognitionbusiness.industryOptical communicationsCognitive neuroscience of visual object recognitionÒptica021001 nanoscience & nanotechnologyAtomic and Molecular Physics and OpticsProcessament òptic de dadesVisualizationHolographic displayAugmented realityOptical data processing0210 nano-technologybusinessImatges Processament Tècniques digitals
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