Search results for "CAMERA"

showing 10 items of 290 documents

Multispectral Imaging using a Stereo Camera: Concept, Design and Assessment

2011

This paper proposes a one-shot six-channel multispectral color image acquisition system using a stereo camera and a pair of optical filters. The two filters from the best pair selected from among readily available filters such that they modify the sensitivities of the two cameras in such a way that they produce optimal estimation of spectral reflectance and/or color are placed in front of the two lenses of the stereo camera. The two images acquired from the stereo camera are then registered for pixel-to-pixel correspondence. The spectral reflectance and/or color at each pixel on the scene are estimated from the corresponding camera outputs in the two images. Both simulations and experiments…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingComputer scienceMultispectral imageComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONlcsh:TK7800-836002 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing01 natural scienceslcsh:Telecommunicationlaw.inventionMultispectral pattern recognitionstereo camera010309 optics[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessinglawCamera auto-calibrationlcsh:TK5101-67200103 physical sciences0202 electrical engineering electronic engineering information engineeringmultispectral imagingComputer visionreflectance estimationPixelColor imagebusiness.industrylcsh:ElectronicsReflectivityLens (optics)020201 artificial intelligence & image processingArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing:Mathematics and natural science: 400::Information and communication science: 420::Simulation visualization signal processing image processing: 429 [VDP]Stereo cameraComputer stereo visionCamera resectioning
researchProduct

A 1.3 megapixel FPGA-based smart camera for high dynamic range real time video

2013

International audience; A camera is able to capture only a part of a high dynamic range scene information. The same scene can be fully perceived by the human visual system. This is true especially for real scenes where the difference in light intensity between the dark areas and bright areas is high. The imaging technique which can overcome this problem is called HDR (High Dynamic Range). It produces images from a set of multiple LDR images (Low Dynamic Range), captured with different exposure times. This technique appears as one of the most appropriate and a cheap solution to enhance the dynamic range of captured environments. We developed an FPGA-based smart camera that produces a HDR liv…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingComputer science[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONVideo camera02 engineering and technologyTone mapping[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processinglaw.invention[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processinglaw0202 electrical engineering electronic engineering information engineeringComputer visionSmart cameraHigh dynamic range[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingCMOS sensorbusiness.industry020206 networking & telecommunicationsFrame rateLight intensityHuman visual system model020201 artificial intelligence & image processingArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
researchProduct

Smart camera design for realtime High Dynamic Range imaging

2011

International audience; Many camera sensors suffer from limited dynamic range. The result is that there is a lack of clear details in displayed images and videos. This paper describes our approach to generate high dynamic range (HDR) from an image sequence while modifying exposure times for each new frame. For this purpose, we propose an FPGA-based architecture that can produce a real-time high dynamic range video from successive image acquisition. Our hardware platform is build around a standard low dynamic range CMOS sensor and a Virtex 5 FPGA board. The CMOS sensor is a EV76C560 provided by e2v. This 1.3 Megapixel device offers novel pixel integration/readout modes and embedded image pre…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingACM IEEEImagingVideosHardware[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingHigh-dynamic-range imaging0202 electrical engineering electronic engineering information engineeringComputer visionSmart cameraImage sensorImage resolutionHigh dynamic range[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingPipelinesCMOS sensorDynamic rangePixelbusiness.industrySensors020208 electrical & electronic engineeringReal time systems020207 software engineeringFrame rate[SPI.TRON]Engineering Sciences [physics]/Electronics[ SPI.TRON ] Engineering Sciences [physics]/ElectronicsArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
researchProduct

HDR-ARtiSt: High Dynamic Range Advanced Real-Time Imaging System

2012

International audience; This paper describes the HDR-ARtiSt hardware platform, a FPGA-based architecture that can produce a real- time high dynamic range video from successive image acquisition. The hardware platform is built around a standard low dynamic range (LDR) CMOS sensor and a Virtex 5 FPGA board. The CMOS sensor is a EV76C560 provided by e2v. This 1.3 Megapixel device offers novel pixel integration/readout modes and em- bedded image pre-processing capabilities including multiframe acquisition with various exposure times. Our approach consists of a hardware architecture with different algorithms: double exposure control during image capture, building of an HDR image by combining the…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputer scienceHardware platformReal-time computingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage processingVideo camera02 engineering and technologyTone mapping[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processinglaw.invention[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessinglawHistogram0202 electrical engineering electronic engineering information engineeringHigh dynamic rangeFPGA[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingHardware architectureCMOS sensorLiquid-crystal displayreal timePixelbusiness.industryDynamic range020207 software engineeringHigh Dynamic RangeFrame rate[SPI.TRON] Engineering Sciences [physics]/Electronics[ SPI.TRON ] Engineering Sciences [physics]/Electronicsimage processing[SPI.TRON]Engineering Sciences [physics]/ElectronicsIEEE020201 artificial intelligence & image processingbusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingComputer hardware
researchProduct

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
researchProduct

A smart camera for High Dynamic Range imaging

2013

International audience; A camera or a video camera is able to capture only a part of a high dynamic range scene information. The same scene can be almost totally perceived by the human visual system. This is true especially for real scenes where the difference in light intensity between the dark areas and bright areas is high. The imaging technique which can overcome this problem is called HDR (High Dynamic Range). It produces images from a set of multiple LDR images (Low Dynamic Range), captured with different exposure times. This technique appears as one of the most appropriate and a cheap solution to enhance the dynamic range of captured environments. We developed an FPGA-based smart cam…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processingsmart camera[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONHigh dynamic range[ 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
researchProduct

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
researchProduct

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
researchProduct

Quantitative aspects of egg-laying behaviour contribute to the eruptive success of Cameraria ohridella parasiting horse-chestnuts.

2015

5 pages; International audience; The invasive leaf-mining moth, Cameraria ohridella, revealed to be a consistent eruptive species throughout Europe, at the expense of its host, the common horse chest-nut tree Aesculus hippocastanum. Its repeated outbreaks, year after year, are admittedly caused, in part, by the inadequacy of the ambient cortege of natural enemies as an effective mean of control of the dynamics of populations of this pest.Less attention has been given to other parameters also contributing to the moth’s impact in term of mines density, such as (i) the degree of selectivity of C. ohridella mothers among host-leaves prior to oviposition and (ii) the average clutch-size.Although…

[ SDE.BE ] Environmental Sciences/Biodiversity and Ecology[ SDV.MP.PAR ] Life Sciences [q-bio]/Microbiology and Parasitology/ParasitologychestnutAesculus[SDV.EE.IEO] Life Sciences [q-bio]/Ecology environment/Symbiosisbehaviour[SDE.BE] Environmental Sciences/Biodiversity and Ecologyleaf-miningparasite[ SDV.EE.IEO ] Life Sciences [q-bio]/Ecology environment/Symbiosisegg[SDV.MP.PAR]Life Sciences [q-bio]/Microbiology and Parasitology/Parasitologymothclutch-size[SDE.BE]Environmental Sciences/Biodiversity and Ecology[SDV.MP.PAR] Life Sciences [q-bio]/Microbiology and Parasitology/ParasitologyCameraria ohridella[SDV.EE.IEO]Life Sciences [q-bio]/Ecology environment/Symbiosis
researchProduct

Event-Based Trajectory Prediction Using Spiking Neural Networks

2021

International audience; In recent years, event-based sensors have been combined with spiking neural networks (SNNs) to create a new generation of bio-inspired artificial vision systems. These systems can process spatio-temporal data in real time, and are highly energy efficient. In this study, we used a new hybrid event-based camera in conjunction with a multi-layer spiking neural network trained with a spike-timing-dependent plasticity learning rule. We showed that neurons learn from repeated and correlated spatio-temporal patterns in an unsupervised way and become selective to motion features, such as direction and speed. This motion selectivity can then be used to predict ball trajectory…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]PolynomialComputer scienceNeuroscience (miscellaneous)Neurosciences. Biological psychiatry. Neuropsychiatry02 engineering and technologyunsupervised learningSNN[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]STDP03 medical and health sciencesCellular and Molecular Neuroscience0302 clinical medicineLearning rule0202 electrical engineering electronic engineering information engineeringEvent (probability theory)Original ResearchSpiking neural networkQuantitative Biology::Neurons and Cognitionmotion selectivitybusiness.industry[SCCO.NEUR]Cognitive science/Neuroscience[SCCO.NEUR] Cognitive science/NeuroscienceProcess (computing)Pattern recognitionspiking cameraTrajectoryball trajectory predictionUnsupervised learning020201 artificial intelligence & image processingArtificial intelligencebusiness030217 neurology & neurosurgeryEfficient energy useNeuroscienceRC321-571Frontiers in Computational Neuroscience
researchProduct