Search results for "Computer Science::Computer Vision and Pattern Recognition"

showing 10 items of 193 documents

Group Nonnegative Matrix Factorization with Sparse Regularization in Multi-set Data

2021

Constrained joint analysis of data from multiple sources has received widespread attention for that it allows us to explore potential connections and extract meaningful hidden components. In this paper, we formulate a flexible joint source separation model termed as group nonnegative matrix factorization with sparse regularization (GNMF-SR), which aims to jointly analyze the partially coupled multi-set data. In the GNMF-SR model, common and individual patterns of particular underlying factors can be extracted simultaneously with imposing nonnegative constraint and sparse penalty. Alternating optimization and alternating direction method of multipliers (ADMM) are combined to solve the GNMF-S…

Computer scienceGroup (mathematics)020206 networking & telecommunications02 engineering and technologySparse approximationNon-negative matrix factorizationSet (abstract data type)Constraint (information theory)Computer Science::Computer Vision and Pattern Recognition0202 electrical engineering electronic engineering information engineeringSource separation020201 artificial intelligence & image processingJoint (audio engineering)Sparse regularizationAlgorithm2020 28th European Signal Processing Conference (EUSIPCO)
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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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Improving light propagation Monte Carlo simulations with accurate 3D modeling of skin tissue

2008

In this paper, we present a 3D light propagation model to simulate multispectral reflectance images of large skin surface areas. In particular, we aim to simulate more accurately the effects of various physiological properties of the skin in the case of subcutaneous vein imaging compared to existing models. Our method combines a Monte Carlo light propagation model, a realistic three-dimensional model of the skin using parametric surfaces and a vision system for data acquisition. We describe our model in detail, present results from the Monte Carlo modeling and compare our results with those obtained with a well established Monte Carlo model and with real skin reflectance images.

Computer scienceMachine visionbusiness.industryQuantitative Biology::Tissues and OrgansPhysics::Medical PhysicsMultispectral imageMonte Carlo methodImage processingSolid modeling3D modelingData acquisitionParametric surfaceComputer Science::Computer Vision and Pattern RecognitionComputer visionArtificial intelligencebusinessBiological system2008 15th IEEE International Conference on Image Processing
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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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Dynamic 3D Scene Reconstruction and Enhancement

2017

International audience; In this paper, we present a 3D reconstruction and enhancement approach for high quality dynamic city scene reconstructions. We first detect and segment the moving objects using 3D Motion Segmenta-tion approach by exploiting the feature trajectories' behaviours. Getting the segmentations of both the dynamic scene parts and the static scene parts, we propose an efficient point cloud registration approach which takes the advantages of 3-point RANSAC and Iterative Closest Points algorithms to produce precise point cloud alignment. Furthermore, we proposed a point cloud smoothing and texture mapping framework to enhance the results of reconstructions for both the static a…

Computer sciencePoint cloudComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingRANSACPoint Cloud Registration0202 electrical engineering electronic engineering information engineeringSegmentationComputer vision3D Scene Enhancement[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingComputingMethodologies_COMPUTERGRAPHICSMotion Segmentationbusiness.industry3D reconstruction020207 software engineeringFeature (computer vision)Computer Science::Computer Vision and Pattern Recognition020201 artificial intelligence & image processingArtificial intelligencebusiness3D Reconstruction[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingTexture mappingSmoothing
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A novel Bayesian framework for relevance feedback in image content-based retrieval systems

2006

This paper presents a new algorithm for image retrieval in content-based image retrieval systems. The objective of these systems is to get the images which are as similar as possible to a user query from those contained in the global image database without using textual annotations attached to the images. The main problem in obtaining a robust and effective retrieval is the gap between the low level descriptors that can be automatically extracted from the images and the user intention. The algorithm proposed here to address this problem is based on the modeling of user preferences as a probability distribution on the image space. Following a Bayesian methodology, this distribution is the pr…

Computer sciencebusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONRelevance feedbackPattern recognitioncomputer.software_genreAutomatic image annotationArtificial IntelligenceComputer Science::Computer Vision and Pattern RecognitionSignal ProcessingProbability distributionComputer Vision and Pattern RecognitionVisual WordArtificial intelligenceData miningbusinessPrecision and recallImage retrievalcomputerSoftwarePattern Recognition
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Reduction of the number of spectral bands in Landsat images: a comparison of linear and nonlinear methods

2006

We describe some applications of linear and nonlinear pro- jection methods in order to reduce the number of spectral bands in Land- sat multispectral images. The nonlinear method is curvilinear component analysis CCA, and we propose an adapted optimization of it for image processing, based on the use of principal-component analysis PCA, a linear method. The principle of CCA consists in reproducing the topol- ogy of the original space projection points in a reduced subspace, keep- ing the maximum of information. Our conclusions are: CCA is an im- provement for dimension reduction of multispectral images; CCA is really a nonlinear extension of PCA; CCA optimization through PCA called CCAinitP…

Computer sciencebusiness.industryDimensionality reductionQuantization (signal processing)Multispectral imageGeneral EngineeringImage processingPattern recognitionImage segmentationSpectral bandsNonlinear Sciences::Cellular Automata and Lattice GasesAtomic and Molecular Physics and OpticsStatistics::Machine LearningComputer Science::Computer Vision and Pattern RecognitionPrincipal component analysisComputer visionArtificial intelligenceProjection (set theory)businessSubspace topologyOptical Engineering
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Image synthesis using the Lau effect

1990

Abstract Based on the Lau effect at finite distances, we describe a lensless optical setup for synthesizing laterally periodic images; which are composed of several, incoherently superimposed, object substructures. Some experimental verifications are also reported.

Computer sciencebusiness.industryObject (computer science)Atomic and Molecular Physics and Opticslaw.inventionElectronic Optical and Magnetic MaterialsImage synthesisLens (optics)OpticslawComputer Science::Computer Vision and Pattern RecognitionElectrical and Electronic EngineeringPhysical and Theoretical ChemistrybusinessMathematicsOptics Communications
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Two-view “cylindrical decomposition” of binary images

2001

This paper describes the discrete cylindrical algebraic decomposition (DCAD) construction along two orthogonal views of binary images. The combination of two information is used to avoid ambiguities for image recognition purposes. This algorithm associates an object connectivity graph to each connected component, allowing a complete description of the structuring information. Moreover, an easy and compact representation of the scene is achieved by using strings in a five letter alphabet. Examples on complex digital images are also provided. © 2001 Elsevier Science Inc.

Connected componentNumerical AnalysisAlgebra and Number TheoryTheoretical computer scienceSettore INF/01 - InformaticaBinary imageObject (computer science)StructuringCylindrical algebraic decompositionString representationDigital imageImage decompositionComputer Science::Computer Vision and Pattern RecognitionDecomposition (computer science)Discrete Mathematics and CombinatoricsGeometry and TopologyRepresentation (mathematics)AlgorithmShape descriptionMathematicsLinear Algebra and its Applications
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Reliable Planar Object Pose Estimation in Light Fields From Best Subaperture Camera Pairs

2018

International audience; A light-field camera can obtain richer information about a scene than a usual camera. This property offers a lot of potential for robot vision. In this paper, we present a method for pose estimation of a planar object with a light-field camera. The light-field camera can be regarded as a set of sub-aperture cameras. Although any combination of them can theoretically be used for the pose estimation, the accuracy depends on the combination. We show that the estimated pose error can be reduced by selecting the best pair of sub-aperture cameras. We have evaluated the accuracy of our approach with real experiments using a light-field camera in front of planar targets held…

Control and OptimizationComputer scienceProperty (programming)Biomedical EngineeringComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONSet (abstract data type)PlanarArtificial Intelligence[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]Computer visionPoseComputer Science::DatabasesGround truthbusiness.industryMechanical EngineeringAstrophysics::Instrumentation and Methods for Astrophysics[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]16. Peace & justiceObject (computer science)Computer Science ApplicationsHuman-Computer InteractionControl and Systems EngineeringComputer Science::Computer Vision and Pattern RecognitionComputer Vision and Pattern RecognitionArtificial intelligencebusiness
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