Search results for "Computer vision"

showing 10 items of 2353 documents

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
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Preprocessing of region of interest localization based on local surface curvature analysis for three-dimensional reconstruction with multiresolution

2009

We present an approach to integrate a preprocessing step of the region of interest ROI localization into 3-D scanners laser or ste- reoscopic. The definite objective is to make the 3-D scanner intelligent enough to localize rapidly in the scene, during the preprocessing phase, the regions with high surface curvature, so that precise scanning will be done only in these regions instead of in the whole scene. In this way, the scanning time can be largely reduced, and the results contain only per- tinent data. To test its feasibility and efficiency, we simulated the prepro- cessing process under an active stereoscopic system composed of two cameras and a video projector. The ROI localization is…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION3d scanningStereoscopyImage processing0102 computer and information sciences02 engineering and technologyIterative reconstruction[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingCurvature01 natural sciencesVideo projectorsurface curvaturelaw.invention[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingRegion of interestlaw0202 electrical engineering electronic engineering information engineeringPreprocessorComputer visionImage resolution[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingComputingMethodologies_COMPUTERGRAPHICSbusiness.industryintelligent 3D scannerGeneral EngineeringAtomic and Molecular Physics and OpticsROI localisation010201 computation theory & mathematics020201 artificial intelligence & image processingArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingadaptive pattern
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Asserting the Precise Position of 3D and Multispectral Acquisition Systems for Multisensor Registration Applied to Cultural Heritage Analysis

2012

International audience; We present a novel method to register multispectral acquisitions on a 3D model. The method is based on the external tracking of the acquisition systems using close-range photogrammetric techniques: multiple calibrated cameras simultaneously observe the successive acquisition systems in use. The views from these cameras are used to precisely determine the position of each acquisition system. All datasets can then be projected in the same coordinate system. The registration is thus independent from the quality and content of the data. This method is well suited to the study of cultural heritage or any other application where we do not wish to place targets on the objec…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputer scienceCoordinate systemMultispectral image02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingclose range photogrammetryTracking (particle physics)multispectral acquisitions[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingPosition (vector)0202 electrical engineering electronic engineering information engineeringComputer vision[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing2d-3d registrationbusiness.industry020207 software engineeringcultural heritageObject (computer science)Pipeline (software)optical calibrationCultural heritage3d digitizationPhotogrammetry020201 artificial intelligence & image processingArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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Registration of 3D and Multispectral Data for the Study of Cultural Heritage Surfaces

2013

International audience; We present a technique for the multi-sensor registration of featureless datasets based on the photogrammetric tracking of the acquisition systems in use. This method is developed for the in situ study of cultural heritage objects and is tested by digitizing a small canvas successively with a 3D digitization system and a multispectral camera while simultaneously tracking the acquisition systems with four cameras and using a cubic target frame with a side length of 500 mm. The achieved tracking accuracy is better than 0.03 mm spatially and 0.150 mrad angularly. This allows us to seamlessly register the 3D acquisitions and to project the multispectral acquisitions on th…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputer scienceMultispectral imageComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technologyclose range photogrammetryTracking (particle physics)computer.software_genrelcsh:Chemical technologyBiochemistryArticle3D digitizationAnalytical Chemistry[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing11. Sustainability2D-3D registration0202 electrical engineering electronic engineering information engineeringmultispectral imagingComputer visionlcsh:TP1-1185Electrical and Electronic EngineeringInstrumentationDigitization[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingMultispectral dataMultimediabusiness.industryFrame (networking)020207 software engineeringcultural heritageAtomic and Molecular Physics and Opticsoptical calibrationCultural heritagePhotogrammetrydigitization020201 artificial intelligence & image processingArtificial intelligencebusinesscomputer[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingSensors
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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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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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Ontology-driven Image Analysis for Histopathological Images

2010

International audience; Ontology-based software and image processing engine must cooperate in new fields of computer vision like microscopy acquisition wherein the amount of data, concepts and processing to be handled must be properly controlled. Within our own platform, we need to extract biological objects of interest in huge size and high-content microscopy images. In addition to specific low-level image analysis procedures, we used knowledge formalization tools and high-level reasoning ability of ontology-based software. This methodology made it possible to improve the expressiveness of the clinical models, the usability of the platform for the pathologist and the sensitivity or sensibi…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputer science[INFO.INFO-IM] Computer Science [cs]/Medical ImagingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage processingOntology (information science)[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]030218 nuclear medicine & medical imaging03 medical and health sciences[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]0302 clinical medicineSoftware[STAT.AP] Statistics [stat]/Applications [stat.AP][INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingDigital image processing[ INFO.INFO-TI ] Computer Science [cs]/Image Processing[INFO.INFO-IM]Computer Science [cs]/Medical ImagingComputer visionRDFImage analysis[STAT.AP]Statistics [stat]/Applications [stat.AP]Information retrieval[ INFO.INFO-IM ] Computer Science [cs]/Medical Imagingbusiness.industry[ STAT.AP ] Statistics [stat]/Applications [stat.AP][INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Usabilitycomputer.file_formatAutomatic image annotation[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV]030220 oncology & carcinogenesis[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]Artificial intelligencebusinesscomputer
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The Kolmogorov superposition theorem and its application to image processing

2010

Best student paper award; International audience

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingKST[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]GeneralLiterature_MISCELLANEOUSimage processing[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingComputingMilieux_COMPUTERSANDEDUCATIONKSN[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingComputingMilieux_MISCELLANEOUS[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
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Entire Reflective Object Surface Structure Understanding

2015

International audience

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingComputer sciencebusiness.industry[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingSurface structureComputer visionArtificial intelligenceObject (computer science)businessComputingMilieux_MISCELLANEOUS
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A classification approach to prostate cancer localization in 3T Multi-Parametric MRI

2016

International audience; Multiparametric-magnetic resonance imaging (mp-MRI) has demonstrated, in many studies, its potential in prostate cancer detection and analysis. We propose a supervised classification approach based on mp-MRI data base of 20 patients, in order to localize prostate cancer and to achieve a cartographic representation of the prostate voxels based on classification results. Proposed method provides a computer aided detection (CAD) software for prostatic cancer. For that, we have extracted varied features providing functional, anatomical and metabolic information helping the classifier to distinguish between three different classes ("Healthy", "Benign" and "Pathologic"). W…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[SPI] Engineering Sciences [physics][INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputer scienceSVMFeature extractionWord error ratecomputer.software_genre030218 nuclear medicine & medical imaging03 medical and health sciencesProstate cancer[SPI]Engineering Sciences [physics]0302 clinical medicine[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingProstateVoxelmedicine[ SPI ] Engineering Sciences [physics]Computer visionProstate cancermedicine.diagnostic_testbusiness.industryPattern recognitionMagnetic resonance imagingSpectramedicine.disease3. Good healthRandom forestSupport vector machinemedicine.anatomical_structuremp-MRIArtificial intelligencebusinesscomputer030217 neurology & neurosurgeryRandom forest
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