Search results for "Linear"

showing 10 items of 7165 documents

Quadratic Objective Functions for Dichromatic Model Parameters Estimation

2017

International audience; In this paper, we present a novel method to estimate dichromatic model parameters from a single color image. Estimation of reflectance, shading and specularity has many applications such as shape recovery, specularity removal and facilitates classical image processing and computer vision tasks such as segmentation or classification. Our method is based on two successive and independent constrained quadratic programming steps to recover the parameters of the model. Compared to recent methods, our approach has the advantage to transform a complex inverse problem into two parralelizable optimization steps that are much easier to solve. We have compared our method with r…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingLinear programmingColor imagebusiness.industry[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]020206 networking & telecommunicationsImage processing02 engineering and technologyInverse problem[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Quadratic equation[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Specularity[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingRobustness (computer science)0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingComputer visionQuadratic programmingArtificial intelligencebusinessAlgorithmMathematics
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Parameter-free adaptive step-size multiobjective optimization applied to remote photoplethysmography

2018

International audience; In this work, we propose to reformulate the objective function of Independent Component Analysis (ICA) to make it a better posed problem in the context of Remote photoplethysmography (rPPG). In recent previous works, linear combination coefficients of RGB channels are estimated maximizing the non-Gaussianity of ICA output components. However, in the context of rPPG a priori knowledge of the pulse signal can be incorporated into the component extraction algorithm. To this end, the contrast function of regular ICA is extended with a measure of periodicity formulated using autocorrelation. This novel semi-blind source extraction method for measuring rPPG has the interes…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingLinear programming[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputer science0206 medical engineeringAutocorrelation[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Context (language use)02 engineering and technology[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]020601 biomedical engineering01 natural sciencesMulti-objective optimizationIndependent component analysis010309 optics[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing0103 physical sciencesA priori and a posterioriRGB color modelLinear combinationAlgorithm
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Detection and matching of curvilinear structures

2011

We propose an approach to curvilinear and wiry object detection and matching based on a new curvilinear region detector (CRD) and a shape context-like descriptor (COH). Standard methods for local patch detection and description are not directly applicable to wiry objects and curvilinear structures, such as roads, railroads and rivers in satellite and aerial images, vessels and veins in medical images, cables, poles and fences in urban scenes, stems and tree branches in natural images, since they assume the object is compact, i.e. that most elliptical patches around features cover only the object. However, wiry objects often have no flat parts and most neighborhoods include both foreground a…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingMatching (graph theory)Computer science[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technology01 natural sciences010309 optics[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingArtificial Intelligence0103 physical sciences0202 electrical engineering electronic engineering information engineeringSegmentationComputer visionComputingMilieux_MISCELLANEOUSCurvilinear coordinatesbusiness.industryObject (computer science)Object detectionTree (data structure)Signal ProcessingPattern recognition (psychology)020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligenceScale (map)business[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingSoftware
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On Some Applications of Nonlinear Differential Equations in Image Processing: Concepts and Electronic Implementation

2011

International audience

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputer scienceAnisotropic diffusionNonlinear image processingImage processing02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingcellular nonlinear networksComputational science[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingNagumoReaction–diffusion system0202 electrical engineering electronic engineering information engineeringReaction-diffusionComputingMilieux_MISCELLANEOUS[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingNonlinear image processing020208 electrical & electronic engineeringanisotropic diffusionNonlinear differential equations[SPI.TRON] Engineering Sciences [physics]/Electronics[SPI.TRON]Engineering Sciences [physics]/Electronics[ SPI.TRON ] Engineering Sciences [physics]/Electronics020201 artificial intelligence & image processing[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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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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COLORED NOISE EFFECTS ON GHOST STOCHASTIC RESONANCE

2014

International audience; We analyze the Ghost Stochastic Resonance (GSR) effect in an electronic circuit exactly ruled by the FitzHugh-Nagumo (FHN) equations, both numerically and experimentally. When the circuit is excited with a bichromatic driving with two close frequencies, we show that for an appropriate noise intensity the circuit response exhibits a ghost frequency which is not present in the biharmonic input signal. In this paper, we highlight the e ects of colored noise on GSR.

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[ NLIN.NLIN-CD ] Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD][ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[SPI.TRON] Engineering Sciences [physics]/Electronics[ SPI.TRON ] Engineering Sciences [physics]/Electronics[SPI.TRON]Engineering Sciences [physics]/Electronics[NLIN.NLIN-CD] Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD][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][NLIN.NLIN-CD]Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD][ NLIN.NLIN-PS ] Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS][SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
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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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Image Encryption/Decryption system based on an oscillatory cellular nonlinear network

2008

International audience

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingimage encryption[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingComputingMilieux_MISCELLANEOUSnon linear dynamics[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
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A comparative study of noise effects in a FitzHugh-Nagumo circuit

2014

International audience; This paper focuses on the behaviour of a nonlinear FitzHugh-Nagumo circuit in the stochastic case that is in presence of noise and without deterministic driving. When the circuit is tuned below the Andronov-Hopf bifurcation, classical coherence res- onance signature is revealed. We compare the stochastic response of the system when the noise acts on two different parameters of the system. It is experimentally shown that an enhancement of the systems response can be achieved when the noise is directly added into the nonlinearity.

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[SPI] Engineering Sciences [physics][INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[ NLIN.NLIN-CD ] Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD][ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[SPI.TRON] Engineering Sciences [physics]/Electronics[SPI.TRON]Engineering Sciences [physics]/Electronics[ SPI.TRON ] Engineering Sciences [physics]/Electronics[SPI]Engineering Sciences [physics][NLIN.NLIN-CD] Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD][NLIN.NLIN-PS]Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS][INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[NLIN.NLIN-PS] Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS][NLIN.NLIN-CD]Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD][ SPI ] Engineering Sciences [physics][ NLIN.NLIN-PS ] Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS][SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
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Predictive Chaos Control for the 1D-map of Action Potential Duration

2016

International audience; In the present work, a nonlinear control method namely predictive controlis investigated. The proposed method allows stabilizing unstable period-1 rhythm.Using mathematical analysis and computer simulations, we show that this methodcan be used to control chaotic behavior or pathological rhythms. As example, theresults are illustrated in the case of the 1D-map action potential duration (APDi+1)which modelizes the cardiac action potential duration as the function of the previousone (APDi).

[ MATH ] Mathematics [math]Action Potential Duration (APD)chaos[ NLIN ] Nonlinear Sciences [physics][NLIN] Nonlinear Sciences [physics][MATH] Mathematics [math][NLIN]Nonlinear Sciences [physics][MATH]Mathematics [math]normal rhythmirregular heart rhythmequilibrium pointpredictive control
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