Search results for " image processing."

showing 10 items of 2265 documents

Reaction-Diffusion Network For Geometric Multiscale High Speed Image Processing

2010

International audience; In the framework of heavy mid-level processing for high speed imaging, a nonlinear bi-dimensional network is proposed, allowing the implementation of active curve algorithms. Usually this efficient type of algorithm is prohibitive for real-time image processing due to its calculus charge and the inadequate structure for the use of serial or parallel architectures. Another kind of implementation philosophy is proposed here, by considering the active curve generated by a propagation phenomenon inspired from biological modeling. A programmable nonlinear reaction-diffusion system is proposed under front control and technological constraints. Geometric multiscale processin…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingComputer scienceNonlinear signal processingImage processing02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingType (model theory)Multiscale geometryComputational scienceImage analysisNonlinear signal processing[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingReaction–diffusion systemDigital image processing0202 electrical engineering electronic engineering information engineeringComputer visionStructure (mathematical logic)Biological modelingbusiness.industry020208 electrical & electronic engineeringNonlinear systemSignal Processing020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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Spatially variant dimensionality reduction for the visualization of multi/hyperspectral images

2011

International audience; In this paper, we introduce a new approach for color visu- alization of multi/hyperspectral images. Unlike traditional methods, we propose to operate a local analysis instead of considering that all the pixels are part of the same population. It takes a segmentation map as an input and then achieves a dimensionality reduction adaptively inside each class of pixels. Moreover, in order to avoid unappealing discon- tinuities between regions, we propose to make use of a set of distance transform maps to weigh the mapping applied to each pixel with regard to its relative location with classes' centroids. Results on two hyperspec- tral datasets illustrate the efficiency of…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingComputer sciencePopulation0211 other engineering and technologiesComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing0202 electrical engineering electronic engineering information engineeringComputer visionSegmentationspectral imageseducationspatially variantvisualization021101 geological & geomatics engineeringdimensionality reductioneducation.field_of_studyPixelbusiness.industryDimensionality reductionHyperspectral imagingIndependent component analysisVisualizationComputer Science::Computer Vision and Pattern Recognition020201 artificial intelligence & image processingArtificial intelligencebusinessDistance transform[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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Validation of a 2D multispectral camera: application to dermatology/cosmetology on a population covering five skin phototypes

2011

International audience; This paper presents the validation of a new multispectral camera specifically developed for dermatological application based on healthy participants from five different Skin PhotoTypes (SPT). The multispectral system provides images of the skin reflectance at different spectral bands, coupled with a neural network-based algorithm that reconstructs a hyperspectral cube of cutaneous data from a multispectral image. The flexibility of neural network based algorithm allows reconstruction at different wave ranges. The hyperspectral cube provides both high spectral and spatial information. The study population involves 150 healthy participants. The participants are classif…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingComputer sciencePopulationMultispectral imageSkin imaging system[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingspectral reflectance01 natural sciencesspectral reconstructionMultispectral pattern recognition010309 optics030207 dermatology & venereal diseases03 medical and health sciencesFitzpatrick scale0302 clinical medicine[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing0103 physical scienceseducationRemote sensingvalidationeducation.field_of_studyHyperspectral imagingSpectral bandshyperspectral cubemultispectral imageFace (geometry)Cube[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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A nonlinear oscillators network devoted to image processing

2004

A contrast enhancement and image inverting tool using a lattice of uncoupled nonlinear oscillators is proposed. We show theoretically and numerically that the gray scale picture contrast is strongly enhanced even if this one is initially very small. An image inversion can be also obtained in real time with the same Cellular Nonlinear Network (CNN) without reconfiguration of the network. A possible electronic implementation of this CNN is finally discussed.

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingComputer science[ PHYS.COND.CM-DS-NN ] Physics [physics]/Condensed Matter [cond-mat]/Disordered Systems and Neural Networks [cond-mat.dis-nn]Image processing[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingCellular nonlinear networksTopology01 natural sciencesGrayscale010305 fluids & plasmasNonlinear oscillators[NLIN.NLIN-PS]Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS][INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingControl theoryLattice (order)0103 physical sciences[ NLIN.NLIN-PS ] Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS][PHYS.COND.CM-DS-NN]Physics [physics]/Condensed Matter [cond-mat]/Disordered Systems and Neural Networks [cond-mat.dis-nn]010306 general physicsEngineering (miscellaneous)ComputingMilieux_MISCELLANEOUSArtificial neural networkApplied MathematicsControl reconfigurationInversion (meteorology)neural networks[ SPI.TRON ] Engineering Sciences [physics]/Electronics[SPI.TRON]Engineering Sciences [physics]/ElectronicsNonlinear systemComputer Science::Computer Vision and Pattern RecognitionModeling and SimulationNonlinear dynamics[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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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
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Region-based segmentation on depth images from a 3D reference surface for tree species recognition.

2013

International audience; The aim of the work presented in this paper is to develop a method for the automatic identification of tree species using Terrestrial Light Detection and Ranging (T-LiDAR) data. The approach that we propose analyses depth images built from 3D point clouds corresponding to a 30 cm segment of the tree trunk in order to extract characteristic shape features used for classifying the different tree species using the Random Forest classifier. We will present the method used to transform the 3D point cloud to a depth image and the region based segmentation method used to segment the depth images before shape features are computed on the segmented images. Our approach has be…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingComputer science[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingFeature extractionPoint cloudComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentation[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technology[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Minimum spanning tree-based segmentation[STAT.AP] Statistics [stat]/Applications [stat.AP][INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[ INFO.INFO-TI ] Computer Science [cs]/Image Processing0202 electrical engineering electronic engineering information engineeringSegmentationComputer vision[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing[STAT.AP]Statistics [stat]/Applications [stat.AP]Contextual image classificationbusiness.industry[ STAT.AP ] Statistics [stat]/Applications [stat.AP][INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]020207 software engineeringPattern recognitionImage segmentation15. Life on landdepth image segmentationRandom forestdepth images from 3D point cloudsIEEE[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV][INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]020201 artificial intelligence & image processingsingle tree species recognitionArtificial intelligenceRange segmentationbusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingForest inventory
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Filtering and emission area identification in the Time Resolved Imaging data

2012

Abstract Time Resolved Imaging (TRI) acquisitions allow precise timing analysis of emission spots. Up to date technologies deeply challenge their isolation by hiding the weak ones, under sizing or over sizing visually detectable emission spots and finally by jeopardizing timing resolution. We report on an algorithm based on 1 and 2D signal processing tools which automates the identification of emission sites and optimizes separation between noise and useful signal, even for weak spots surrounding strong emission areas. The application of the algorithm on several sets of data from different types of devices and their results are also discussed.

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingComputer science[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[SPI.NANO] Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technology01 natural sciencesImaging dataSignal[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing0103 physical sciences0202 electrical engineering electronic engineering information engineeringIsolation (database systems)[SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing010302 applied physicsSignal processingNoise (signal processing)business.industryPhoto EmissionStatic timing analysisPattern recognitionSizingIdentification (information)IC Failure AnalysisImage Thresholding[ SPI.NANO ] Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics020201 artificial intelligence & image processingArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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Remote Photoplethysmography Based on Implicit Living Skin Tissue Segmentation

2016

International audience; Region of interest selection is an essential part for remote photoplethysmography (rPPG) algorithms. Most of the time, face detection provided by a supervised learning of physical appearance features coupled with skin detection is used for region of interest selection. However, both methods have several limitations and we propose to implicitly select living skin tissue via their particular pulsatility feature. The input video stream is decomposed into several temporal superpixels from which pulse signals are extracted. Pulsatility measure for each temporal superpixel is then used to merge pulse traces and estimate the photoplethysmogram signal. This allows to select …

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingComputer science[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing0206 medical engineering[INFO.INFO-IM] Computer Science [cs]/Medical Imaging02 engineering and technology[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]01 natural sciences010309 optics[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingSkin tissueRegion of interestPhotoplethysmogram0103 physical sciences[INFO.INFO-IM]Computer Science [cs]/Medical ImagingSegmentationComputer visionFace detection[ SDV.IB.IMA ] Life Sciences [q-bio]/Bioengineering/Imaging[ INFO.INFO-IM ] Computer Science [cs]/Medical Imagingbusiness.industrySupervised learning[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]020601 biomedical engineering[SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/ImagingArtificial intelligencebusiness
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An evaluation framework and a benchmark for multi/hyperspectral image compression

2011

International audience; This paper benchmarks three multi/hyperspectral image compression approaches: the classic Multi-2D compression approach and two different implementations of 3D approach (Full 3D and Hybrid). All approaches are combined with a spectral PCA decorrelation stage to optimize performance. These three compression approaches are compared within a larger comparison framework than the conventionally used PSNR, which includes eight metrics divided into three families. The comparison is carried out with regard to variations in bitrates, spatial, and spectral dimensions variations of images. The time and memory consumption difference between the three approaches is also discussed…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingComputer sciencebusiness.industryMultispectral image0211 other engineering and technologiesPattern recognition02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingcompressionwaveletsWavelet[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingCompression (functional analysis)Hyperspectral image compression0202 electrical engineering electronic engineering information engineeringBenchmark (computing)020201 artificial intelligence & image processingArtificial intelligencebusinessDecorrelation[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingMulti/hyperspectral images021101 geological & geomatics engineeringImage compression
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Analysis of an Experimental Model of In Vitro Cardiac Tissue Using Phase Space Reconstruction

2014

International audience; The in vitro cultures of cardiac cells represent valuable models to study the mechanism of the arrhythmias at the cellular level. But the dynamics of these experimental models cannot be characterized precisely, as they include a lot of parameters that depend on experimental conditions. This paper is devoted to the investigation of the dynamics of an in vitro model using a phase space reconstruction. Our model, based on the heart cells of new born rats, generates electrical field potentials acquired using a microelectrode technology, which are analyzed in normal and under external stimulation conditions. Phase space reconstructions of electrical field potential signal…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingField (physics)Quantitative Biology::Tissues and OrgansChaoticHealth Informatics[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing030204 cardiovascular system & hematology01 natural sciences03 medical and health sciencesTheoretical physicsNonlinear system0302 clinical medicineDimension (vector space)[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingPhase space0103 physical sciencesSignal ProcessingAttractorEmbedding010306 general physicsBiological system[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingStatistical hypothesis testingMathematics
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