Search results for " Graphics"

showing 10 items of 594 documents

A Constrained Band Selection Method Based on Information Measures for Spectral Image Color Visualization

2011

International audience; We present a new method for the visualization of spectral images, based on a selection of three relevant spectral channels to build a Red-Green-Blue composite. Band selection is achieved by means of information measures at the first, second and third orders. Irrelevant channels are preliminarily removed by means of a center-surround entropy comparison. A visualization-oriented spectrum segmentation based on the use of color matching functions allows for computational ease and adjustment of the natural rendering. Results from the proposed method are presented and objectively compared to four other dimensionality reduction techniques in terms of naturalness and informa…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing010504 meteorology & atmospheric sciencesBand SelectionComputer science0211 other engineering and technologies02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing01 natural sciencesRendering (computer graphics)Spectrum SegmentationData visualization[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingColor Matching FunctionsEntropy (information theory)Computer visionSegmentationElectrical and Electronic Engineering021101 geological & geomatics engineering0105 earth and related environmental sciencesVisualizationInformation Measuresbusiness.industryDimensionality reductionPattern recognitionImage segmentationVisualizationMulti/hyperspectral imageryGeneral Earth and Planetary SciencesArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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Manufactured object sub-segmentation based on reflection motion estimation

2015

International audience; In computer vision, reflection is a long-standing problem, it covers image textures, makes original color difficult to recognize, complicates the understanding of the scene. Most of the time, it is considered as “noise”. Many methods are proposed in order to reduce or delete the reflection effects in the image, but generally, the performances are not quite satisfactory. While instead of working on “de-noising”, we propose a method to take advantage of moving reflections that can be used for different computer vision applications. For instance, the segmentation of reflective manufactured objects is presented in this paper. We focus on tracking reflection components an…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingSegmentation-based object categorizationbusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentationPattern recognition02 engineering and technologyImage segmentation01 natural sciencesScale space010309 opticsImage texture[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingRegion growingMotion estimation0103 physical sciences0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingComputer visionArtificial intelligenceReflection (computer graphics)businessMathematics
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Reconstruction of hyperspectral cutaneous data from an artificial neural network-based multispectral imaging system.

2011

International audience; The development of an integrated MultiSpectral Imaging (MSI) system yielding hyperspectral cubes by means of artificial neural networks is described. The MSI system is based on a CCD camera, a rotating wheel bearing a set of seven interference filters, a light source and a computer. The resulting device has been elaborated for in vivo imaging of skin lesions. It provides multispectral images and is coupled with a software reconstructing hyperspectral cubes from multispectral images. Reconstruction is performed by a neural network-based algorithm using heteroassociative memories. The resulting hyperspectral cube provides skin optical reflectance spectral data combined…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputer scienceMultispectral imageHealth InformaticsDermoscopy[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing01 natural sciencesSensitivity and SpecificitySkin DiseasesMultispectral pattern recognition010309 opticsImaging systemSoftware[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingInterference (communication)0103 physical sciencesImage Interpretation Computer-AssistedSkin cancerHumansRadiology Nuclear Medicine and imagingComputer visionSpatial analysis[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingSpectral reflectanceRadiological and Ultrasound TechnologyArtificial neural networkbusiness.industryMultispectral images010401 analytical chemistryHyperspectral imagingReproducibility of ResultsEquipment DesignComputer Graphics and Computer-Aided Design0104 chemical sciencesEquipment Failure AnalysisHyperspectral cube reconstructionColorimetryComputer Vision and Pattern RecognitionArtificial intelligenceNeural Networks Computerbusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingPreclinical imagingNeural networksFiltrationComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
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Noise estimation from digital step-model signal

2013

International audience; This paper addresses the noise estimation in the digital domain and proposes a noise estimator based on the step signal model. It is efficient for any distribution of noise because it does not rely only on the smallest amplitudes in the signal or image. The proposed approach uses polarized/directional derivatives and a nonlinear combination of these derivatives to estimate the noise distribution (e.g., Gaussian, Poisson, speckle, etc.). The moments of this measured distribution can be computed and are also calculated theoretically on the basis of noise distribution models. The 1D performances are detailed, and as our work is mostly dedicated to image processing, a 2D…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processingstep model02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingCCD sensornoise distributionsymbols.namesake[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processingdigital signalsalt and pepper noiseStatistics0202 electrical engineering electronic engineering information engineeringMedian filterImage noisePoisson noiseValue noiseNoise estimationMathematics[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingedge modelmultiplicative noiseNoise measurementNoise (signal processing)020206 networking & telecommunicationsComputer Graphics and Computer-Aided DesignNoise floorGaussian white noiseGradient noiseimpulse noiseGaussian noisenonlinear modelsymbols020201 artificial intelligence & image processingnoise estimatorAlgorithm[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingSoftware
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3D part inspection path planning of a laser scanner with control on the uncertainty

2011

International audience; This article concerns the measurement process of mechanical parts using laser scanners. From the point of view of industrial applications, the objective is to guarantee the measurement accuracy during the scanning with regard to the geometrical product specifications. The proposed method can be summarized as follow: the first step consists of analyzing the interval of tolerance for the different specifications and to attribute to every geometrical entity a maximal uncertainty of measurement. This uncertainty depends on the angle of incidence between the laser plane and the scanned surface. In the second step, an approach based on the concept of visibility is used fro…

[ SPI.MECA.GEME ] Engineering Sciences [physics]/Mechanics [physics.med-ph]/Mechanical engineering [physics.class-ph]0209 industrial biotechnologyEngineeringLaser scanningPoint cloud[PHYS.MECA.GEME]Physics [physics]/Mechanics [physics]/Mechanical engineering [physics.class-ph]02 engineering and technologyInterval (mathematics)01 natural sciencesIndustrial and Manufacturing Engineeringthree-dimensional inspectionlaser sensor010309 optics020901 industrial engineering & automation0103 physical sciencesComputer visionPoint (geometry)Motion planninguncertainties of measurementbusiness.industryscanning pathVisibility (geometry)visibilityComputer Graphics and Computer-Aided DesignComputer Science ApplicationsMetrology[SPI.MECA.GEME]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Mechanical engineering [physics.class-ph][ PHYS.MECA.GEME ] Physics [physics]/Mechanics [physics]/Mechanical engineering [physics.class-ph]Measurement uncertaintyArtificial intelligencebusiness
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LDR Image to HDR Image Mapping with Overexposure Preprocessing

2013

International audience; Due to the growing popularity of High Dynamic Range (HDR) images and HDR displays, a large amount of existing Low Dynamic Range (LDR) images are required to be converted to HDR format to benefit HDR advantages, which give rise to some LDR to HDR algorithms. Most of these algorithms especially tackle overexposed areas during expanding, which is the potential to make the image quality worse than that before processing and introduces artifacts. To dispel these problems, we . present a new,LDR to HDR approach, unlike the existing techniques, it focuses on avoiding sophisticated treatment to overexposed areas in dynamic range expansion step. Based on a separating principl…

[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR]Image qualityComputer scienceImage mapPrincipal component analysisComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONHDR02 engineering and technologyImage (mathematics)Highlight removal0202 electrical engineering electronic engineering information engineeringPreprocessorComputer visionElectrical and Electronic EngineeringComputingMilieux_MISCELLANEOUSHigh dynamic rangeExposurebusiness.industryDynamic rangeApplied MathematicsImage quality metric020207 software engineeringComputer Graphics and Computer-Aided DesignOverexposed areaSignal ProcessingMetric (mathematics)020201 artificial intelligence & image processing[ INFO.INFO-AR ] Computer Science [cs]/Hardware Architecture [cs.AR]Artificial intelligencebusinessIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
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Design of a Real-time face detection parallel architecture using High-Level Synthesis

2008

Abstract We describe a High-Level Synthesis implementation of a parallel architecture for face detection. The chosen face detection method is the well-known Convolutional Face Finder (CFF) algorithm, which consists of a pipeline of convolution operations. We rely on dataflow modelling of the algorithm and we use a high-level synthesis tool in order to specify the local dataflows of our Processing Element (PE), by describing in C language inter-PE communication, fine scheduling of the successive convolutions, and memory distribution and bandwidth. Using this approach, we explore several implementation alternatives in order to find a compromise between processing speed and area of the PE. We …

[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR][INFO.INFO-AR] Computer Science [cs]/Hardware Architecture [cs.AR]General Computer ScienceVideo Graphics ArrayComputer scienceDataflowlcsh:Electronicslcsh:TK7800-8360020207 software engineering02 engineering and technologyParallel computing020202 computer hardware & architectureConvolutionScheduling (computing)Control and Systems EngineeringHigh-level synthesis0202 electrical engineering electronic engineering information engineeringParallel architecture[ INFO.INFO-AR ] Computer Science [cs]/Hardware Architecture [cs.AR]ArchitectureFace detectionComputingMilieux_MISCELLANEOUSComputer Science(all)
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Modélisation géométrique de formes fractales pour la CAO

2020

International audience

[INFO.INFO-CG] Computer Science [cs]/Computational Geometry [cs.CG][MATH.MATH-GT]Mathematics [math]/Geometric Topology [math.GT][MATH.MATH-DS]Mathematics [math]/Dynamical Systems [math.DS][INFO.INFO-GR] Computer Science [cs]/Graphics [cs.GR]ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS/I.3.5: Computational Geometry and Object Modeling[MATH.MATH-DS] Mathematics [math]/Dynamical Systems [math.DS][INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation[INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG][INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationComputingMilieux_MISCELLANEOUS[INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR][MATH.MATH-GT] Mathematics [math]/Geometric Topology [math.GT]
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Close-Range Photogrammetric Tools for Epigraphic Surveys

2016

In this article, we present a method to carry out a computerized epigraphic survey of historic stones and their engraved epigraphy. In fact, the preservation of archaeological objects has always been an issue of concern for the research community. On the one hand, the fragility of the objects limits their study. On the other hand, such objects are housed in museums, libraries, and institutions worldwide, locations that significantly limit their accessibility. Different survey methods have been carried out in these attempts to overcome the aforementioned limitations. Among them, the MicMac open source software was used in this work to survey and process the dense correlation of the hieroglyp…

[SHS.ARCHEO] Humanities and Social Sciences/Archaeology and Prehistory[SHS.ARCHEO]Humanities and Social Sciences/Archaeology and Prehistory020207 software engineering02 engineering and technologyConservationOpen source softwareComputer Graphics and Computer-Aided DesignArchaeologyComputer Science ApplicationsClose rangeEpigraphyCultural heritageSurvey methodologyGeographyPhotogrammetryResearch community11. Sustainability0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingComputingMilieux_MISCELLANEOUSInformation Systems
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Contextualization of archaeological information using augmented photospheres, viewed with head-mounted displays.

2019

Photospheres, or 360&deg

[SHS.ARCHEO]Humanities and Social Sciences/Archaeology and PrehistoryComputer scienceGeography Planning and Developmentlcsh:TJ807-830lcsh:Renewable energy sourcesprojectionManagement Monitoring Policy and LawSpace (commercial competition)Virtual reality01 natural sciences050105 experimental psychologyComputer graphicsDocumentationProcrustesregistration11. Sustainability0501 psychology and cognitive sciencesthin-plate splinelcsh:Environmental sciencesvisualizationlcsh:GE1-350ContextualizationRenewable Energy Sustainability and the Environmentlcsh:Environmental effects of industries and plants010401 analytical chemistry05 social sciencesarchaeologycultural heritageArchaeology0104 chemical sciencesVisualizationCultural heritagelcsh:TD194-195computer graphicsvirtual realityscientific mediation
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