Search results for "Signal and Image processing"

showing 10 items of 454 documents

eISP: a Programmable Processing Architecture for Smart Phone Image Enhancement

2009

4 pages; Today's smart phones, with their embedded high-resolution video sensors, require computing capacities that are too high to easily meet stringent silicon area and power consumption requirements (some one and a half square millimeters and half a watt) especially when programmable components are used. To develop such capacities, integrators still rely on dedicated low resolution video processing components, whose drawback is low flexibility. With this in mind, our paper presents eISP {--} a new, fully programmable Embedded Image Signal Processor architecture, now validated in {TSMC 65nm} technology to achieve a capacity of {16.8 GOPs} at {233 MHz}, for {1.5 mm$^2$} of silicon area and…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processinglow power[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingCMOSdemosaïcking[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingeISPmm²SIMDimage processingvideo pipesmall siliconMulti-SIMDcomputing tilemilliwatt[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingsensordemosaicingTSMC 65nm[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
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Camera-based measurement of relative image contrast in projection displays

2013

International audience; This research investigated the measured contrast of projection displays based on pictures taken by un-calibrated digital cameras under typical viewing conditions. A high-end radiometer was employed as a reference to the physical response of projection luminance. Checkerboard, gray scale and color complex test images with a range of the projector's brightness and contrast settings were projected. Two local and two global contrast metrics were evaluated on the acquired pictures. We used contrast surface plots and Pearson correlation to investigate the measured contrast versus the projector's brightness and contrast settings. The results suggested, as expected, the proj…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processingoptical projectors[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processingprojection luminanceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingmeasured contrastradiometersImage color analysismetrics[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processingstatistical analysispicture acquisitioncamerasgray scaleBrightnessoptical variables measurementdigital cameracamera-based measurementRadiometry[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingdisplay devicesDigital camerasuncalibrated digital cameraglobal contrast metricsprojector brightness settingscheckerboardcolor complex test imagesrelative image contrastviewing conditionsradiometerimage processingCorrelationPearson correlationhigh-end radiometerprojection displayprojector contrast settings[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingprojection displayscontrast surface plotsstatistic based metrics
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AN APPROACH TO CORRECTING IMAGE DISTORTION BY SELF CALIBRATION STEREOSCOPIC SCENE FROM MULTIPLE VIEWS

2012

International audience; An important step in the analysis and interpretation of video scenes for recognizing scenario is the aberration corrections introduced during the image acquisition in order to provide and correct real image data. This paper presents an approach on distortion correction based on stereoscopic self calibration from images sequences by using a multi-camera system of vision (network cameras). This approach for correcting image distortion brings an elegant and robust technique with good accuracy. Without any knowledge of shooting conditions, the camera's parameters will be estimated. For this, the image key points of interest are extracted from different overlapping views …

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processingprojective rectification[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingImage qualityEpipolar geometryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage processing[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technologyfundamental matrix[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingCamera auto-calibration0202 electrical engineering electronic engineering information engineeringComputer visionImage rectificationImage warpingImage restorationstereovision[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingMathematicsbusiness.industry020208 electrical & electronic engineeringAstrophysics::Instrumentation and Methods for AstrophysicsReal imageComputer Science::Computer Vision and Pattern Recognitionepipolar geometry020201 artificial intelligence & image processingArtificial intelligencedistortionbusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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Definition of a mutual reference shape based on information theory and active contours

2013

In this paper, we propose to consider the estimation of a reference shape from a set of different segmentation results using both active contours and information theory. The reference shape is then defined as the minimum of a criterion that benefits from both the mutual information and the joint entropy of the input segmentations. This energy criterion is here justified using similarities between information theory quantities and area measures, and presented in a continuous variational framework. This framework brings out some interesting evaluation measures such as the specificity and sensitivity. In order to solve this shape optimization problem, shape derivatives are computed for each te…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processingsegmentation evaluation[ INFO.INFO-IM ] Computer Science [cs]/Medical Imaging[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processingaverage shape[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging[INFO.INFO-IM] Computer Science [cs]/Medical ImagingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingactive contours[SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV]shape gradientsImage processingcardiac MRI.[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV][ INFO.INFO-TI ] Computer Science [cs]/Image Processingshape optimizationcardiac MRI[INFO.INFO-IM]Computer Science [cs]/Medical Imaging[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[ SDV.IB.IMA ] Life Sciences [q-bio]/Bioengineering/Imaging[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processinginformation theory
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Roughness evaluation of vine leaf by image processing

2013

International audience; The study of leaf surface roughness is very important in the domain of precision spraying. It is one of the parameters that allow to reduce costs and losses of phytosanitary prod- ucts and to improve the spray accuracy. Moreover, the leaf roughness is related to adhesion mechanisms of liquid on a surface. It can be used to define leaf nature surface (hy- drophilic/hydrophobic). The main goal of this study is thus to estimate and to follow the evolution of leaf roughness using image processing and computer vision. The develop- ment and application of computer vision for measurement of surface leaf roughness using artificial neural networks will be described. The syste…

[ MATH ] Mathematics [math]0106 biological sciences0209 industrial biotechnologyScanning electron microscope[SDV]Life Sciences [q-bio]Computer Vision[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[MATH] Mathematics [math]02 engineering and technologySurface finishLeaf roughness01 natural sciences[PHYS] Physics [physics][SPI]Engineering Sciences [physics]020901 industrial engineering & automation[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[ SPI ] Engineering Sciences [physics]Surface roughnessComputer vision[MATH]Mathematics [math]ComputingMilieux_MISCELLANEOUS[PHYS]Physics [physics][ PHYS ] Physics [physics]Artificial neural network[STAT]Statistics [stat]Multilayer perceptron[SDE]Environmental SciencesBiological system[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingMaterials science[ STAT ] Statistics [stat][INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[SPI] Engineering Sciences [physics]IASTEDFast Fourier transformNeural NetworkImage processingImage processing[SDV.BV]Life Sciences [q-bio]/Vegetal BiologyTexturelanguage technologies[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingPrecision agriculturebusiness.industry[STAT] Statistics [stat]Precision agricultureArtificial intelligencebusiness010606 plant biology & botany
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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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Modélisation, Analyse et Traitement de l'Information

2016

Mes activités de recherche s’articulent, d’une part, autour de l’instrumentation et du génie biomédical,et, d’autre part, autour du traitement et de la transmission non linéaire de l’information. Elles sebasent sur la modélisation des signaux à partir de modèles non linéaires (principalement modèles deréaction-diffusion. . . ) continus (EDP) et discrets (numériques). Dans cette partie, d’un point de vuefondamental, des phénomènes dynamiques complexes ou chaotiques sont caractérisés à travers l’analyse,la classification, la reconnaissance des motifs dans des signaux physiologiques ou issus des circuitsélectroniques. Un autre axe sur lequel je travaille concerne l’analyse et le traitement des…

[ NLIN ] Nonlinear Sciences [physics]Reconnaissance de motifsSystèmes électroniquesDynamique non linéaire[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingAnalyse Multirésolution[SPI.TRON] Engineering Sciences [physics]/ElectronicsModelling[ SPI.TRON ] Engineering Sciences [physics]/Electronics[SPI.TRON]Engineering Sciences [physics]/ElectronicsclassificationModélisationFiltrageNonlinear dynamics[NLIN] Nonlinear Sciences [physics][NLIN]Nonlinear Sciences [physics]ElectronicsFilteringPattern matchingInstrumentation[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingGénie biomédical
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Optimisation conjointe de la taille de stockage et des performances de modèles de classification pour l’authentification de visages

2017

International audience

[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingComputingMilieux_MISCELLANEOUS[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
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Dark Count rate measurement in Geiger mode and simulation of a photodiode array, with CMOS 0.35 technology and transistor quenching.

2014

International audience; Some decades ago single photon detection used to be the terrain of photomultiplier tube (PMT), thanks to its characteristics of sensitivity and speed. However, PMT has several disadvantages such as low quantum efficiency, overall dimensions, and cost, making them unsuitable for compact design of integrated systems. So, the past decade has seen a dramatic increase in interest in new integrated single-photon detectors called Single-Photon Avalanche Diodes (SPAD) or Geiger-mode APD. SPAD detectors fabricated in a standard CMOS technology feature both single-photon sensitivity, and excellent timing resolution, while guarantying a high integration. SPAD are working in ava…

[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
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Automatic emission spots identification in static and dynamic imaging by research of local maxima.

2014

International audience

[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[SPI.TRON] Engineering Sciences [physics]/ElectronicsComputingMilieux_MISCELLANEOUS[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing[SPI.TRON]Engineering Sciences [physics]/Electronics[ SPI.TRON ] Engineering Sciences [physics]/Electronics
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