Search results for "Pixel"

showing 10 items of 421 documents

Complex networks : application for texture characterization and classification

2008

This article describes a new method and approch of texture characterization. Using complex network representation of an image, classical and derived (hierarchical) measurements, we presente how to have good performance in texture classification. Image is represented by a complex networks : one pixel as a node. Node degree and clustering coefficient, using with traditionnal and extended hierarchical measurements, are used to characterize ”organisation” of textures.

Computer engineering. Computer hardwareTexture compressionComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONComplex networksImage processingTexture (geology)TK7885-7895Image textureImage processingAnàlisi de texturaProcesamiento de imágenestexture analysisClustering coefficientAnálisis de texturaRedes complejasPixelbusiness.industryNode (networking)Pattern recognitionProcessament d'imatgescomplex networksQA75.5-76.95Xarxes complexesComplex networkTexture analysisElectronic computers. Computer scienceComputer Science::Computer Vision and Pattern RecognitionComputer Vision and Pattern RecognitionArtificial intelligencebusinessSoftwareELCVIA: electronic letters on computer vision and image analysis
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Multimodal Images Classification using Dense SURF, Spectral Information and Support Vector Machine

2019

International audience; The multimodal image classification is a challenging area of image processing which can be used to examine the wall painting in the cultural heritage domain. In such classification, a common space of representation is important. In this paper, we present a new method for multimodal representation learning, by using a pixel-wise feature descriptor named dense Speed Up Robust Features (SURF) combined with the spectral information carried by the pixel. For classification of extracted features we have used support vector machine (SVM). Our database was extracted from acquisition on cultural heritage wall paintings that contain four modalities UV, Visible, IRR and fluores…

Computer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage processing02 engineering and technologyImage (mathematics)0202 electrical engineering electronic engineering information engineeringFeature descriptorRepresentation (mathematics)Spectral informationSpeeded up robust features SURFGeneral Environmental SciencePixelbusiness.industry[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]020206 networking & telecommunicationsPattern recognitionSVM classificationSupport vector machineCultural heritageMultimodal imagesCielab spaceDense features[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]General Earth and Planetary Sciences020201 artificial intelligence & image processingArtificial intelligencebusinessFeature learning
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Exudates as Landmarks Identified through FCM Clustering in Retinal Images

2020

The aim of this work was to develop a method for the automatic identification of exudates, using an unsupervised clustering approach. The ability to classify each pixel as belonging to an eventual exudate, as a warning of disease, allows for the tracking of a patient&rsquo

Computer scienceDiabetic retinopathy; Exudates; Fuzzy C-means clustering; Morphological processing; Retinal landmarks; SegmentationFundus (eye)Fuzzy logiclcsh:TechnologyField (computer science)030218 nuclear medicine & medical imaginglcsh:Chemistry03 medical and health sciences0302 clinical medicineFcm clusteringfuzzy C-means clusteringretinal landmarksGeneral Materials ScienceSegmentationSensitivity (control systems)Cluster analysisInstrumentationlcsh:QH301-705.5Fluid Flow and Transfer ProcessesSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniPixelSettore INF/01 - Informaticabusiness.industrylcsh:TProcess Chemistry and TechnologyexudatessegmentationGeneral EngineeringPattern recognitionlcsh:QC1-999Computer Science Applicationsdiabetic retinopathyComputingMethodologies_PATTERNRECOGNITIONlcsh:Biology (General)lcsh:QD1-999lcsh:TA1-2040Artificial intelligencebusinesslcsh:Engineering (General). Civil engineering (General)030217 neurology & neurosurgerylcsh:Physicsmorphological processingApplied Sciences
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A video-based real-time vehicle counting system using adaptive background method

2008

International audience; This paper presents a video-based solution for real time vehicle detection and counting system, using a surveillance camera mounted on a relatively high place to acquire the traffic video stream.The two main methods applied in this system are: the adaptive background estimation and the Gaussian shadow elimination. The former allows a robust moving detection especially in complex scenes. The latter is based on color space HSV, which is able to deal with different size and intensity shadows. After these two operations, it obtains an image with moving vehicle extracted, and then operation counting is effected by a method called virtual detector.

Computer scienceGaussianComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technologyHSL and HSVColor spaceVideo analysisShadow eliminationAdaptive background estimationImage (mathematics)symbols.namesake0502 economics and businessShadow0202 electrical engineering electronic engineering information engineeringComputer visionSurveillance camera050210 logistics & transportationPixelbusiness.industry05 social sciencesDetectorVirtual detectorsymbols020201 artificial intelligence & image processingArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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A new Adaptive and Progressive Image Transmission Approach using Function Superpositions

2010

International audience; We present a novel approach to adaptive and progressive image transmission, based on the decomposition of an image into compositions and superpositions of monovariate functions. The monovariate functions are iteratively constructed and transmitted, one after the other, to progressively reconstruct the original image: the progressive transmission is performed directly in the 1D space of the monovariate functions and independently of any statistical properties of the image. Each monovariate function contains only a fraction of the pixels of the image. Each new transmitted monovariate function adds data to the previously transmitted monovariate functions. After each tra…

Computer scienceImage qualityComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technologyIterative reconstructionmultidimensional function decompositionSuperposition principleRobustness (computer science)[ INFO.INFO-TI ] Computer Science [cs]/Image Processing0202 electrical engineering electronic engineering information engineeringComputer visionsignal processingspatial scalability.Image resolutionImage restorationSignal processingPixelbusiness.industryprogressive image transmissionGeneral Engineering020206 networking & telecommunicationsAtomic and Molecular Physics and Opticsfunctional representation[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]Computer Science::Computer Vision and Pattern RecognitionKolmogorov superposition theorem020201 artificial intelligence & image processingTomographyArtificial intelligencebusinessDigital filterAlgorithmspatial scalabilityImage compression
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A Comparison of Advanced Regression Algorithms for Quantifying Urban Land Cover

2014

Quantitative methods for mapping sub-pixel land cover fractions are gaining increasing attention, particularly with regard to upcoming hyperspectral satellite missions. We evaluated five advanced regression algorithms combined with synthetically mixed training data for quantifying urban land cover from HyMap data at 3.6 and 9 m spatial resolution. Methods included support vector regression (SVR), kernel ridge regression (KRR), artificial neural networks (NN), random forest regression (RFR) and partial least squares regression (PLSR). Our experiments demonstrate that both kernel methods SVR and KRR yield high accuracies for mapping complex urban surface types, i.e., rooftops, pavements, gras…

Computer scienceLand coverimaging spectrometrysub-pixel mappingKernel (linear algebra)urban land coverPartial least squares regressionlcsh:Sciencespatial resolutionHyMapRemote sensingmachine learning; regression; sub-pixel mapping; spatial resolution; imaging spectrometry; hyperspectral; urban land coverTraining setArtificial neural networkbusiness.industryHyperspectral imagingPattern recognitionRandom forestSupport vector machineKernel methodmachine learninghyperspectralKernel (statistics)General Earth and Planetary Sciencesregressionlcsh:QArtificial intelligencebusinessRemote Sensing
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High-resolution far-field integral-imaging camera by double snapshot

2012

In multi-view three-dimensional imaging, to capture the elemental images of distant objects, the use of a field-like lens that projects the reference plane onto the microlens array is necessary. In this case, the spatial resolution of reconstructed images is equal to the spatial density of microlenses in the array. In this paper we report a simple method, based on the realization of double snapshots, to double the 2D pixel density of reconstructed scenes. Experiments are reported to support the proposed approach.

Computer scienceMotion PicturesComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONNear and far fieldlaw.inventionImaging Three-DimensionalOpticslawPhotographyHumansImage resolutionFatigueLensesMicrolensDepth PerceptionIntegral imagingbusiness.industryPhotographyAccommodation OcularEquipment DesignConvergence OcularAtomic and Molecular Physics and OpticsLens (optics)Computer Science::Computer Vision and Pattern RecognitionDepth perceptionbusinessAlgorithmsPixel densityOptics Express
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Structured Output SVM for Remote Sensing Image Classification

2011

Traditional kernel classifiers assume independence among the classification outputs. As a consequence, each misclassification receives the same weight in the loss function. Moreover, the kernel function only takes into account the similarity between input values and ignores possible relationships between the classes to be predicted. These assumptions are not consistent for most of real-life problems. In the particular case of remote sensing data, this is not a good assumption either. Segmentation of images acquired by airborne or satellite sensors is a very active field of research in which one tries to classify a pixel into a predefined set of classes of interest (e.g. water, grass, trees,…

Computer scienceMultispectral imageTheoretical Computer ScienceSet (abstract data type)Kernel (linear algebra)One-class classificationRemote sensingSupport vector machinesStructured support vector machinePixelContextual image classificationbusiness.industryKernel methodsPattern recognitionLand use classificationSupport vector machineTree (data structure)Kernel methodHardware and ArchitectureControl and Systems EngineeringModeling and SimulationKernel (statistics)Radial basis function kernelSignal ProcessingStructured output learningArtificial intelligenceTree kernelStructured output learning; Support vector machines; Kernel methods; Land use classificationbusinessInformation SystemsJournal of Signal Processing Systems
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New Method of Microimages Generation for 3D Display

2018

In this paper, we propose a new method for the generation of microimages, which processes real 3D scenes captured with any method that permits the extraction of its depth information. The depth map of the scene, together with its color information, is used to create a point cloud. A set of elemental images of this point cloud is captured synthetically and from it the microimages are computed. The main feature of this method is that the reference plane of displayed images can be set at will, while the empty pixels are avoided. Another advantage of the method is that the center point of displayed images and also their scale and field of view can be set. To show the final results, a 3D InI dis…

Computer sciencePoint cloud3D displaylcsh:Chemical technologyStereo display01 natural sciencesBiochemistryArticleAnalytical Chemistry010309 opticsSet (abstract data type)Depth mapplenoptic imaging0103 physical scienceslcsh:TP1-1185Point (geometry)Computer visionElectrical and Electronic EngineeringInstrumentation010302 applied physicsMicrolensIntegral imagingPixelbusiness.industryÒpticaAtomic and Molecular Physics and Opticsintegral imagingFeature (computer vision)Artificial intelligenceCGIIbusinessImatges Processament Tècniques digitalsSensors
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The Athena X-ray Integral Field Unit (X-IFU)

2016

Event: SPIE Astronomical Telescopes + Instrumentation, 2016, Edinburgh, United Kingdom.

Computer science[ PHYS.ASTR ] Physics [physics]/Astrophysics [astro-ph]AstronomyObservatoriesField of viewAthena; Instrumentation; Space telescopes; X-ray Integral Field Unit; X-ray spectroscopy; Electronic Optical and Magnetic Materials; Condensed Matter Physics; Computer Science Applications1707 Computer Vision and Pattern Recognition; Applied Mathematics; Electrical and Electronic Engineering7. Clean energy01 natural scienceslaw.inventionSettore FIS/05 - Astronomia E AstrofisicalawObservatoryAthena Instrumentation Space telescopes X-ray spectroscopy X-ray Integral Field UnitAthena010303 astronomy & astrophysicsInstrumentation[ PHYS.PHYS.PHYS-INS-DET ] Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det]SpectroscopyHigh Energy Astrophysical Phenomena (astro-ph.HE)Equipment and servicesApplied MathematicsX-rayComputer Science Applications1707 Computer Vision and Pattern RecognitionCondensed Matter PhysicsProceedings of SPIE - the International Society for Optical EngineeringX-ray spectroscopyAstrophysics - Instrumentation and Methods for AstrophysicsAstrophysics - High Energy Astrophysical PhenomenaHigh energy astrophysicsAstrophysics - Cosmology and Nongalactic AstrophysicsCosmology and Nongalactic Astrophysics (astro-ph.CO)Spectral resolutionFOS: Physical sciencesMinute of arcSpace telescopesTelescope0103 physical sciencesX-raysElectronicOptical and Magnetic Materials[PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det]Spectral resolutionElectrical and Electronic Engineering010306 general physicsSpectroscopyInstrumentation and Methods for Astrophysics (astro-ph.IM)Remote sensingPixelAstrophysics - Astrophysics of GalaxiesAstrophysics of Galaxies (astro-ph.GA)X-ray Integral Field Unit[PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph]Telescopes
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