Search results for " Pattern Recognition"

showing 10 items of 1050 documents

Shape from polarization: a method for solving zenithal angle ambiguity

2012

International audience; We report a multispectral based method that permits the evolution of shape from polarization setup applied to 3D shape estimation of transparent objects. The setup is based on a polarization imaging technique which is a recent imaging method based on the analysis of the polarization state of the light in the observed scene. The technique has rapidly evolved with the development of electro-optic components and some polarization cameras are now available on the market. Shape from polarization consists in measuring the azimuthal and zenithal angles characterizing the normal of each point of the observed surface. We focus on the ambiguity in the measurement of the zenith…

Physicspolarization imaging[SPI.OPTI] Engineering Sciences [physics]/Optics / Photonicbusiness.industrymedia_common.quotation_subject010401 analytical chemistryMultispectral imagePolarization imagingAmbiguityPolarization (waves)01 natural sciencesAtomic and Molecular Physics and Optics3D shape0104 chemical sciences010309 opticsAzimuthOpticsComputer Science::Computer Vision and Pattern Recognition0103 physical sciences[SPI.OPTI]Engineering Sciences [physics]/Optics / Photonic[ SPI.OPTI ] Engineering Sciences [physics]/Optics / PhotonicbusinessRefractive indexmedia_common
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Variable-Radius Offset Surface Approximation on the GPU

2020

Variable-radius offset surfaces find applications in various fields, such as variable brush strokes in 2D and 3D sketching and geometric modeling tools. In forensic facial reconstruction the skin surface can be inferred from a given skull by computing a variable-radius offset surface of the skull surface. Thereby, the skull is represented as a two-manifold triangle mesh and the facial soft tissue thickness is specified for each vertex of the mesh. We present a method to interactively visualize the wanted skin surface by rendering the variable-radius offset surfaces of all triangles of the skull mesh. We have also developed a special shader program which is able to generate a discretized vol…

Physicsshader založený na přiblížení tvaruComputer Science::GraphicsOffset (computer science)variable-radius offsettingComputer Science::Computer Vision and Pattern RecognitionQuantitative Biology::Tissues and Organsoffset s proměnným poloměremPhysics::Medical PhysicsMinkowského sumaGeometryMinkowski sumShader based shape approximationComputer Science Research Notes
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Characterisation and mitigation of beam-induced backgrounds observed in the ATLAS detector during the 2011 proton-proton run

2013

This paper presents a summary of beam-induced backgrounds observed in the ATLAS detector and discusses methods to tag and remove background contaminated events in data. Triggerrate based monitoring of beam-related backgrounds is presented. The correlations of backgrounds with machine conditions, such as residual pressure in the beam-pipe, are discussed. Results from dedicated beam-background simulations are shown, and their qualitative agreement with data is evaluated. Data taken during the passage of unpaired, i.e. non-colliding, proton bunches is used to obtain background-enriched data samples. These are used to identify characteristic features of beam-induced backgrounds, which then are …

Physics::Instrumentation and DetectorsAccelerator modelling and simulations; multi-particle dynamics; Analysis and statistical methods; Pattern recognition cluster finding calibration and fitting methods; Performance of High Energy Physics Detectors; single-particle dynamicsPROTON BEAMSMonte Carlo methodsingle-particle dynamics01 natural sciencesaccelerator modelling and simulations (multi-particle dynamics; single-particle dynamics)High Energy Physics - ExperimentHigh Energy Physics - Experiment (hep-ex)MUON DETECTORcluster findingPIXEL DETECTORSNaturvetenskap[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]GeneralLiterature_REFERENCE(e.g.dictionariesencyclopediasglossaries)InstrumentationQCMathematical PhysicsPhysicsLarge Hadron ColliderPattern recognition cluster finding calibration and fitting methodsAccelerator modelling and simulations (multi-particle dynamics; single-particle dynamics)Settore FIS/01 - Fisica SperimentaleObservableATLAScalibration and fitting methodsAccelerator modelling and simulationsCalorimetermedicine.anatomical_structureBunchesAccelerator Modelling and Simulations (Multi-Particle Dynamics Single-Particle Dynamics)Analysis and statistical methodsLHCmulti-particle dynamicsNatural SciencesParticle Physics - ExperimentParticle physicsCiências Naturais::Ciências Físicas530 PhysicsInstrumentationCALORIMETERS:Ciências Físicas [Ciências Naturais]FOS: Physical sciencesddc:500.2530Nuclear physicssingle-particle dynamics)Atlas (anatomy)Pattern recognition0103 physical sciencesmedicineAccelerator modelling and simulations (multi-particle dynamics single-particle dynamics)High Energy Physicspattern recognition; cluster finding; calibration and fitting methods; single-particle dynamics); analysis and statistical methods; accelerator modelling and simulations (multi-particle dynamics; performance of high energy physics detectorsddc:610010306 general physicsCalibration and fitting methodsScience & Technology010308 nuclear & particles physicsCluster findingFísicaAccelerator modelling and simulations (multi-particle dynamicsAccelerator modelling and simulations (multi-particle dynamics; Analysis and statistical methods; Pattern recognition cluster finding calibration and fitting methods; Performance of High Energy Physics Detectors; single-particle dynamics); Instrumentation; Mathematical PhysicsExperimental High Energy PhysicsPattern recognition; cluster finding; calibration and fitting methods; Performance of High Energy Physics Detectors; Accelerator modelling and simulations (multi-particle dynamics; single-particle dynamics); Analysis and statistical methodsPhysics::Accelerator PhysicsPerformance of High Energy Physics DetectorsEvent (particle physics)
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Color Image Segmentation: The Hypergraph Framework

2006

International audience; Color Image Segmentation: The Hypergraph Framework

Physics::Popular PhysicsMathematics::Combinatorics[ INFO ] Computer Science [cs]Computer Science::Discrete MathematicsComputer Science::Computer Vision and Pattern RecognitionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[INFO]Computer Science [cs][INFO] Computer Science [cs]ComputingMilieux_MISCELLANEOUSComputer Science::Computers and SocietyMathematicsofComputing_DISCRETEMATHEMATICS
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Post-processing of Pixel and Object-Based Land Cover Classifications of Very High Spatial Resolution Images

2020

The state of the art is plenty of classification methods. Pixel-based methods include the most traditional ones. Although these achieved high accuracy when classifying remote sensing images, some limits emerged with the advent of very high-resolution images that enhanced the spectral heterogeneity within a class. Therefore, in the last decade, new classification methods capable of overcoming these limits have undergone considerable development. Within this research, we compared the performances of an Object-based and a Pixel-Based classification method, the Random Forests (RF) and the Object-Based Image Analysis (OBIA), respectively. Their ability to quantify the extension and the perimeter…

PixelComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONObject basedLand coverClass (biology)Random forestObject-Based image analysisRemote sensing (archaeology)Computer Science::Computer Vision and Pattern RecognitionVector based generalizationHigh spatial resolutionObject-Based image analysis; Random forest; Vector based generalizationState (computer science)Settore ICAR/06 - Topografia E CartografiaRandom forestRemote sensing
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Space variant vision and pipelined architecture for time to impact computation

2002

Image analysis is one of the most interesting ways for a mobile vehicle to understand its environment. One of the tasks of an autonomous vehicle is to get accurate information of what it has in front, to avoid collision or find a way to a target. This task requires real-time restrictions depending on the vehicle speed and external object movement. The use of normal cameras, with homogeneous (squared) pixel distribution, for real-time image processing, usually requires high performance computing and high image rates. A different approach makes use of a CMOS space-variant camera that yields a high frame rate with low data bandwidth. The camera also performs the log-polar transform, simplifyin…

PixelComputer sciencebusiness.industryComputationBandwidth (signal processing)ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage processingRemotely operated underwater vehicleFrame rateComputer Science::Computer Vision and Pattern RecognitionDigital image processingComputer visionArtificial intelligencebusinessField-programmable gate array
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Speeding-Up Differential Motion Detection Algorithms Using a Change-Driven Data Flow Processing Strategy

2007

A constraint of real-time implementation of differential motion detection algorithms is the large amount of data to be processed. Full image processing is usually the classical approach for these algorithms: spatial and temporal derivatives are calculated for all pixels in the image despite the fact that the majority of image pixels may not have changed from one frame to the next. By contrast, the data flow model works in a totally different way as instructions are only fired when the data needed for these instructions are available. Here we present a method to speed-up low level motion detection algorithms. This method is based on pixel change instead of full image processing and good spee…

PixelComputer sciencebusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage processingMotion detectionData flow diagramMotion fieldComputer Science::Computer Vision and Pattern RecognitionMotion estimationDigital image processingComputer visionArtificial intelligencebusinessAlgorithmFeature detection (computer vision)
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A support vector domain method for change detection in multitemporal images

2010

This paper formulates the problem of distinguishing changed from unchanged pixels in multitemporal remote sensing images as a minimum enclosing ball (MEB) problem with changed pixels as target class. The definition of the sphere-shaped decision boundary with minimal volume that embraces changed pixels is approached in the context of the support vector formalism adopting a support vector domain description (SVDD) one-class classifier. SVDD maps the data into a high dimensional feature space where the spherical support of the high dimensional distribution of changed pixels is computed. Unlike the standard SVDD, the proposed formulation of the SVDD uses both target and outlier samples for defi…

PixelComputer sciencebusiness.industryFeature vectorComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONThresholdingMultispectral pattern recognitionSupport vector machineKernel methodArtificial IntelligenceComputer Science::Computer Vision and Pattern RecognitionSignal ProcessingOutlierDecision boundaryComputer visionComputer Vision and Pattern RecognitionArtificial intelligencebusinessSoftwareChange detectionPattern Recognition Letters
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A Clustering Approach to texture Classification

1988

In the paper a clustering technique to segment an image in to “homogeneous” regions is studied. The homogeneity of each region is evaluated by means of a “proximity function” computed between the pixels. The main result of such approach is that no-histogramming is required in order to perform segmentation. Possibilistic and probabilistic approaches are, also, combined to evaluate the significativity of the computed regions.

PixelComputer sciencebusiness.industryFeature vectorHomogeneity (statistics)Correlation clusteringComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONProbabilistic logicPattern recognitionImage textureComputer Science::Computer Vision and Pattern RecognitionSegmentationArtificial intelligenceCluster analysisbusiness
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Shape Description for Content-Based Image Retrieval

2000

The present work is focused on a global image characterization based on a description of the 2D displacements of the different shapes present in the image, which can be employed for CBIR applications.To this aim, a recognition system has been developed, that detects automatically image ROIs containing single objects, and classifies them as belonging to a particular class of shapes.In our approach we make use of the eigenvalues of the covariance matrix computed from the pixel rows of a single ROI. These quantities are arranged in a vector form, and are classified using Support Vector Machines (SVMs). The selected feature allows us to recognize shapes in a robust fashion, despite rotations or…

PixelContextual image classificationbusiness.industryComputer scienceCovariance matrixComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage processingPattern recognitionContent-based image retrievalSupport vector machineComputingMethodologies_PATTERNRECOGNITIONFeature (computer vision)Computer Science::Computer Vision and Pattern RecognitionPattern recognition (psychology)Computer visionArtificial intelligencebusiness
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