Search results for "Signal Processing"

showing 10 items of 2451 documents

Integration of 3D and multispectral data for cultural heritage applications: Survey and perspectives

2013

International audience; Cultural heritage is increasingly put through imaging systems such as multispectral cameras and 3D scanners. Though these acquisition systems are often used independently, they collect complementary information (spectral vs. spatial) used for the study, archiving and visualization of cultural heritage. Recording 3D and multispectral data in a single coordinate system enhances the potential insights in data analysis. Wepresent the state of the art of such acquisition systems and their applications for the study of cultural her- itage. Wealso describe existing registration techniques that can be used to obtain 3D models with multispec- tral texture and explore the idea…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingRegistration[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputer scienceMultispectral imageComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION3d model[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technologycomputer.software_genre3D digitizationMultispectral imaging[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing11. Sustainability0202 electrical engineering electronic engineering information engineering[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingMultispectral dataMultimedia020207 software engineeringData fusionSensor fusionData scienceVisualizationCultural heritagePhotogrammetryPhotogrammetrySignal ProcessingCultural heritage020201 artificial intelligence & image processingComputer Vision and Pattern Recognition[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingcomputerImage and Vision Computing
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Scene-based noise reduction on a smart camera

2012

International audience; Raw output data from CMOS image sensors tends to exhibit significant noise called Fixed-Pattern Noise (FPN) due to on-die variations between pixel photodetectors. FPN is often corrected by subtracting its value, estimated through calibration, from the sensor's raw signal. This paper introduces an on-line scene-based technique for an improved FPN compensation which does not rely on calibration, and hence is more robust to the dynamic changes in the FPN which may occur slowly over time. Development has been done with a special emphasis on real-time hardware implementation on a FPGA-based smart camera. Experimental results on different scenes are depicted showing that t…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputer scienceNoise reductionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing01 natural sciencesSignalCompensation (engineering)010309 optics[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing0103 physical sciences0202 electrical engineering electronic engineering information engineeringComputer visionSmart cameraImage sensor[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingPixelNoise (signal processing)business.industry020208 electrical & electronic engineeringEmphasis (telecommunications)Artificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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Kolmogorov Superposition Theorem and Its Application to Multivariate Function Decompositions and Image Representation

2008

International audience; In this paper, we present the problem of multivariate function decompositions into sums and compositions of monovariate functions. We recall that such a decomposition exists in the Kolmogorov's superposition theorem, and we present two of the most recent constructive algorithms of these monovariate functions. We first present the algorithm proposed by Sprecher, then the algorithm proposed by Igelnik, and we present several results of decomposition for gray level images. Our goal is to adapt and apply the superposition theorem to image processing, i.e. to decompose an image into simpler functions using Kolmogorov superpositions. We synthetise our observations, before …

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingImage processing[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technologySuperposition theorem01 natural sciences[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[ INFO.INFO-TI ] Computer Science [cs]/Image ProcessingComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATION0202 electrical engineering electronic engineering information engineeringApplied mathematics0101 mathematics[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingMathematicsDiscrete mathematicsSignal processingArtificial neural network010102 general mathematicsApproximation algorithmSpline (mathematics)[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV]Kolmogorov structure function[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]020201 artificial intelligence & image processingHypercube[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing2008 IEEE International Conference on Signal Image Technology and Internet Based Systems
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Noise removal using a nonlinear two-dimensional diffusion network

1998

Un reseau electrique non lineaire bidimensionnel, constitue de N×N cellules identiques, et modelisant l’equation de Nagumo discrete est presente. A l’aide d’une nouvelle description de la fonction non lineaire, on peut predire analytiquement l’evolution temporelle de la partie coherente du signal, ainsi que celle des perturbations de petites amplitudes qui lui sont superposees. Enfin, des applications a l’amelioration du rapport signal sur bruit, ou au traitement d’images sont suggerees.

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingNoise reductionDiffusion networkImage processing[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing01 natural sciences010305 fluids & plasmassymbols.namesakeSignal-to-noise ratio[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[NLIN.NLIN-PS]Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS][NLIN.NLIN-PS] Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS]0103 physical sciencesElectronic engineering[ NLIN.NLIN-PS ] Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS]Electrical and Electronic Engineering010306 general physics[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingMathematicsSignal processingMathematical analysisWhite noiseNonlinear systemGaussian noisesymbols[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingAnnales Des Télécommunications
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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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Automatic detection of P, QRS and T patterns in 12 leads ECG signal based on CWT

2016

International audience; In this paper, a new method based on the continuous wavelet transform is described in order to detect the QRS, P and T waves. QRS, P and T waves may be distinguished from noise, baseline drift or irregular heartbeats. The algorithm, described in this paper, has been evaluated using the Computers in Cardiology (CinC) Challenge 2011 database and also applied on the MIT-BIH Arrhythmia database (MITDB). The data from the CinC Challenge 2011 are standard 12 ECG leads recordings with full diagnostic bandwidth compared to the MITDB which only includes two leads for each ECG signal. Firstly, our algorithm is validated using fifty 12 leads ECG samples from the CinC collection…

[ MATH ] Mathematics [math][ INFO ] Computer Science [cs]Computer science0206 medical engineeringYouden's J statisticHealth Informatics[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technologyQRS[SPI]Engineering Sciences [physics]QRS complexT waveT waves0202 electrical engineering electronic engineering information engineering[ SPI ] Engineering Sciences [physics][INFO]Computer Science [cs][MATH]Mathematics [math]wavelet transformContinuous wavelet transformECGPdelineationECGP waveWavelet transformP020601 biomedical engineering3. Good healthSignal Processing020201 artificial intelligence & image processingEcg leadEcg signalAlgorithm[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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GW170814: A Three-Detector Observation of Gravitational Waves from a Binary Black Hole Coalescence

2017

On August 14, 2017 at 10 30:43 UTC, the Advanced Virgo detector and the two Advanced LIGO detectors coherently observed a transient gravitational-wave signal produced by the coalescence of two stellar mass black holes, with a false-alarm rate of 1 in 27 000 years. The signal was observed with a three-detector network matched-filter signal-to-noise ratio of 18. The inferred masses of the initial black holes are 30.5-3.0+5.7M and 25.3-4.2+2.8M (at the 90% credible level). The luminosity distance of the source is 540-210+130 Mpc, corresponding to a redshift of z=0.11-0.04+0.03. A network of three detectors improves the sky localization of the source, reducing the area of the 90% credible regio…

[ PHYS.ASTR ] Physics [physics]/Astrophysics [astro-ph]AstronomyCredible regionsGeneral Physics and Astronomyadvanced ligoADVANCED LIGOAstrophysicsdetector: network01 natural sciencesGeneral Relativity and Quantum CosmologylocalizationVIRGO detectorFilter signalsGW170814TOOLLIGOInterferometerGeneralLiterature_REFERENCE(e.g.dictionariesencyclopediasglossaries)010303 astronomy & astrophysicsQCchoiceQBHigh Energy Astrophysical Phenomena (astro-ph.HE)PhysicsSignal to noise ratioSettore FIS/01 - Fisica SperimentaleGravitational effectstoolFalse alarm rateCHOICEAntenna responseGravitational-wave signalsDetector networks[PHYS.GRQC]Physics [physics]/General Relativity and Quantum Cosmology [gr-qc]Astrophysics - High Energy Astrophysical Phenomenagravitational radiation: polarizationSignal processingAstrophysics::High Energy Astrophysical Phenomenablack hole: binary: coalescenceFOS: Physical sciencesGeneral Relativity and Quantum Cosmology (gr-qc)Astrophysics::Cosmology and Extragalactic Astrophysicsgravitational radiation: direct detectionGravitational-wave astronomy[ PHYS.GRQC ] Physics [physics]/General Relativity and Quantum Cosmology [gr-qc]General Relativity and Quantum CosmologyPhysics and Astronomy (all)Binary black hole0103 physical sciencesGW151226ddc:530KAGRASTFCGw150914GW170814 Virgo LIGO010308 nuclear & particles physicsGravitational wavePhysiqueVirgogravitational radiationAstronomyRCUKMatched filtersblack hole: massStarsLIGOgravitational radiation detectorBlack holeradiationVIRGOPhysics and AstronomyTesting Relativistic Gravitygravitationgravitational radiation: emissionStellar-mass black holesRADIATIONStellar black holeHigh Energy Physics::ExperimentAntennasDewey Decimal Classification::500 | Naturwissenschaften::530 | Physik[PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph]
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Enhancement and assessment of WKS variance parameter for intelligent 3D shape recognition and matching based on MPSO

2016

This paper presents an improved wave kernel signature (WKS) using the modified particle swarm optimization (MPSO)-based intelligent recognition and matching on 3D shapes. We select the first feature vector from WKS, which represents the 3D shape over the first energy scale. The choice of this vector is to reinforce robustness against non-rigid 3D shapes. Furthermore, an optimized WKS-based method for extracting key-points from objects is introduced. Due to its discriminative power, the associated optimized WKS values with each point remain extremely stable, which allows for efficient salient features extraction. To assert our method regarding its robustness against topological deformations,…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][ INFO ] Computer Science [cs]Matching (graph theory)Feature vectorComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technology[INFO] Computer Science [cs][ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Kernel (linear algebra)[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Discriminative modelRobustness (computer science)0202 electrical engineering electronic engineering information engineeringFeature (machine learning)[INFO]Computer Science [cs][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]ComputingMilieux_MISCELLANEOUSMathematicsbusiness.industryParticle swarm optimization[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]020207 software engineeringPattern recognition020201 artificial intelligence & image processingArtificial intelligencebusinessEnergy (signal processing)
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Une architecture programmable de traitement des impulsions zéro-temps mort pour l'instrumentation nucléaire

2015

In the field of nuclear instrumentation, digital signal processing architectures have to deal with the poissonian characteristic of the signal, composed of random arrival pulses which requires current architectures to work in dataflow. Thus, the real-time needs implies losing pulses when the pulse rate is too high. Current architectures paralyze the acquisition of the signal during the pulse processing inducing a time during no signal can be processed, this is called the dead time. These issue have led current architectures to use dedicated solutions based on reconfigurable components such as FPGAs. The requirement of end users to implement a wide range of applications on a large number of …

[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR]Architecture électroniqueInstrumentation nucléaireRadioactivité[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingDigital Signal Processing (DSP)traitement du signalNuclear instrumentation[PHYS.NEXP]Physics [physics]/Nuclear Experiment [nucl-ex]Distributed computing[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingTraitement numérique du signal (TNS)Électronique numériqueMesureArchitecture électronique distribuée[PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det]Digital Pulse Processing (DPP)signal processingTraitement numérique des impulsions (DPP)
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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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