Search results for "Signal and Image processing"

showing 10 items of 454 documents

Temporal Denoising of Kinect Depth Data

2012

The release of the Microsoft Kinect has attracted the attention of researchers in a variety of computer science domains. Even though this device is still relatively new, its recent applications have shown some promising results in terms of replacing current conventional methods like the stereo-camera for robotics navigation, multi-camera system for motion detection and laser scanner for 3D reconstruction. While most work around the Kinect is on how to take full advantage of its capabilities, so far only a few studies have been carried out on the limitations of this device and fewer that provide solutions to enhance the precision of its measurements. In this paper, we review and analyse curr…

depth measurement[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingLaser scanning[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputer scienceNoise reductionDenoising algorithmComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technology[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processingtemporal denoising0202 electrical engineering electronic engineering information engineeringComputer visionImage denoisingComputingMilieux_MISCELLANEOUS[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingbusiness.industry3D reconstruction020206 networking & telecommunicationsRoboticsMotion detectionKinect depth dataMicrosoft Kinect020201 artificial intelligence & image processingArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing2012 Eighth International Conference on Signal Image Technology and Internet Based Systems
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Regularization Preserving Localization of Close Edges

2007

International audience; In this letter, we address the problem of the influence of neighbor edges and their effect on the edge delocalization while extracting a neighbor contour by a derivative approach. The properties to be fulfilled by the regularization operators to minimize or suppress this side effect are deduced, and the best detectors are pointed out. The study is carried out in 1-D for discrete signal. We show that among the derivative filters, one of them can correctly detect our model edges without being influenced by a neighboring transition, whatever their separation distance is and their respective amplitude is. A model of contour and close transitions is presented and used through…

edge localization[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processingneighbor edge[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technologyEdge detectionDiscrete-time signal[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[ INFO.INFO-TI ] Computer Science [cs]/Image Processing0202 electrical engineering electronic engineering information engineeringRegularization operatorCanny edge detectorEdge detectionElectrical and Electronic EngineeringMathematicsedge modelbusiness.industryApplied MathematicsDetector020207 software engineeringPattern recognitionregularization filterDeriche edge detectorAmplitude[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]Regularization (physics)Signal Processing020201 artificial intelligence & image processingArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingAlgorithmIEEE Signal Processing Letters
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Overview of ghost correction for HDR video stream generation

2015

International audience; Most digital cameras use low dynamic range image sensors, these LDR sensors can capture only a limited luminance dynamic range of the scene[1], to about two orders of magnitude (about 256 to 1024 levels). However, the dynamic range of real-world scenes varies over several orders of magnitude (10.000 levels). To overcome this limitation, several methods exist for creating high dynamic range (HDR) image (expensive method uses dedicated HDR image sensor and low-cost solutions using a conventional LDR image sensor). Large number of low-cost solutions applies a temporal exposure bracketing. The HDR image may be constructed with a HDR standard method (an additional step ca…

exposure bracketingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONbitmapGraph-Cuts[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingGeneralLiterature_MISCELLANEOUSghost detectionsmart camerahigh dynamic rageentropyreal-time algorithm[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingComputingMethodologies_COMPUTERGRAPHICS
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Definition and performance evaluation of a robust SVM based fall dectection system

2012

International audience

fall detection[ 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 ProcessingSVM[ 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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A Curvature Based Method for Blind Mesh Visual Quality Assessment Using a General Regression Neural Network

2016

International audience; No-reference quality assessment is a challenging issue due to the non-existence of any information related to the reference and the unknown distortion type. The main goal is to design a computational method to objectively predict the human perceived quality of a distorted mesh and deal with the practical situation when the reference is not available. In this work, we design a no reference method that relies on the general regression neural network (GRNN). Our network is trained using the mean curvature which is an important perceptual feature representing the visual aspect of a 3D mesh. Relatively to the human subjective scores, the trained network successfully asses…

feature learning[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingComputer sciencemedia_common.quotation_subjectFeature extractiondistorted meshGRNNmean curvature02 engineering and technologyMachine learningcomputer.software_genreCurvaturevisual aspect representation[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingDistortioncomputational method0202 electrical engineering electronic engineering information engineeringFeature (machine learning)computational geometrymean opinion scoresQuality (business)Polygon meshmedia_commonArtificial neural networkbusiness.industrycompetitive scores Author Keywords Blind mesh visual quality assessmentperceptual feature020207 software engineeringregression analysis INSPEC: Non-Controlled Indexing curvature based methodblind mesh visual quality assessmentno-reference quality assessmentvisual qualityVisualizationgeneral regression neural network traininggeneral regression neural networkmesh generationneural netssubject scoreshuman perceived quality predictionhuman subjective scores020201 artificial intelligence & image processinglearning (artificial intelligence)Artificial intelligencepredicted objective scoresbusiness3D meshcomputer
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Classifying DME vs Normal SD-OCT volumes: A review

2016

International audience; This article reviews the current state of automatic classification methodologies to identify Diabetic Macular Edema (DME) versus normal subjects based on Spectral Domain OCT (SD-OCT) data. Addressing this classification problem has valuable interest since early detection and treatment of DME play a major role to prevent eye adverse effects such as blindness. The main contribution of this article is to cover the lack of a public dataset and benchmark suited for classifying DME and normal SD-OCT volumes, providing our own implementation of the most relevant methodologies in the literature. Subsequently, 6 different methods were implemented and evaluated using this comm…

genetic structuresComputer scienceDiabetic macular edemaEarly detection[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingMachine learningcomputer.software_genre01 natural sciences010309 optics03 medical and health sciences0302 clinical medicinebenchmark0103 physical sciencesmedicine[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingRetinaBlindnessbusiness.industryMachine Learning (ML)medicine.diseaseeye diseasesSpectral Domain OCT (SD-OCT)medicine.anatomical_structure030221 ophthalmology & optometryBenchmark (computing)Artificial intelligenceData miningsense organsDiabetic Macular Edema (DME)businesscomputer[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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Classification of SD-OCT Volumes with LBP: Application to DME Detection

2015

International audience; This paper addresses the problem of automatic classification of Spectral Domain OCT (SD-OCT) data for automatic identification of patients with Diabetic Macular Edema (DME) versus normal subjects. Our method is based on Local Binary Patterns (LBP) features to describe the texture of Optical Coherence Tomography (OCT) images and we compare different LBP features extraction approaches to compute a single signature for the whole OCT volume. Experimental results with two datasets of respectively 32 and 30 OCT volumes show that regardless of using low or high level representations, features derived from LBP texture have highly discriminative power. Moreover, the experimen…

genetic structuresLocal binary patternsComputer scienceDiabetic macular edemaSpectral domain02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicineOptical coherence tomographyDiscriminative modelLBP0202 electrical engineering electronic engineering information engineeringmedicineDMEComputer vision[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingmedicine.diagnostic_testbusiness.industryeye diseasesDiabetic Macular EdemaOCT020201 artificial intelligence & image processingArtificial intelligencesense organsOptical Coherence Tomographybusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection

2016

International audience; This paper addresses the problem of automatic classification of Spectral Domain OCT (SD-OCT) data for automatic identification of patients with Diabetic Macular Edema (DME) versus normal subjects. Optical Coherence Tomography (OCT) has been a valuable diagnostic tool for DME, which is among the most common causes of irreversible vision loss in individuals with diabetes. Here, a classification framework with five distinctive steps is proposed and we present an extensive study of each step. Our method considers combination of various pre-processings in conjunction with Local Binary Patterns (LBP) features and different mapping strategies. Using linear and non-linear cl…

genetic structures[INFO.INFO-IM] Computer Science [cs]/Medical Imaging[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]0302 clinical medicine[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Segmentationlcsh:OphthalmologySpeckleLBPDiagnosisPrevalencePreprocessorComputer visionSegmentationmedicine.diagnostic_test[ INFO.INFO-IM ] Computer Science [cs]/Medical ImagingExperimental validationDiabetic Macular Edema[ SDV.MHEP.OS ] Life Sciences [q-bio]/Human health and pathology/Sensory OrgansOptical Coherence Tomography[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingResearch ArticleArticle SubjectLocal binary patterns03 medical and health sciencesSpeckle patternOptical coherence tomography[ SDV.MHEP ] Life Sciences [q-bio]/Human health and pathologyMedical imagingmedicineDME[INFO.INFO-IM]Computer Science [cs]/Medical ImagingCoherence (signal processing)Texture[SDV.MHEP.OS]Life Sciences [q-bio]/Human health and pathology/Sensory OrgansRetinopathy[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingbusiness.industry[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Pattern recognitioneye diseasesOphthalmologyOCTlcsh:RE1-994030221 ophthalmology & optometryImagesArtificial intelligencebusiness030217 neurology & neurosurgery[SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
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Modern Multispectral Sensors Help Track Explosive Eruptions

2013

Due to its massive air traffic impact, the 2010 eruption of Eyjafjallajokull was felt by millions of people and cost airlines more than U.S. $1.7 billion. The event has, thus, become widely cited in renewed efforts to improve real-time tracking of volcanic plumes, as witnessed by special sections published last year in Journal of Geophysical Research, (117, issues D20 and B9).

geographyExplosive eruptiongeography.geographical_feature_category010504 meteorology & atmospheric sciencesMeteorologyStrombolian Eruptions Multi-sensor field surveyMultispectral imageAir traffic control010502 geochemistry & geophysicsTrack (rail transport)01 natural sciencesAeronauticsVolcano[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG][INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing13. Climate action[SDU]Sciences of the Universe [physics]General Earth and Planetary SciencesGeologyComputingMilieux_MISCELLANEOUS0105 earth and related environmental sciences
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Automatic analyzis of droplet impact by high speed imaging

2012

International audience; The impact of agricultural activities on the water quality is the consequence of the loss of fertilisers (chemical fertilisers, livestock effluent, also referred to as farm fertiliser, food-processing effluent and sludge) and crop treatment products (phytosanitary products). This pollution may prevent certain uses of water, notably its use for human and animal food (groundwater and surface water), and leads to a deterioration in aquatic environments. In the domain of vineyard precision spraying research, one of the most important objectives is to minimize the volume of phytosanitary products. It is also to be more environmentally respectful with more effective vine l…

high speed imaging;spraying application;weber number;active contoursprecision agriculture[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[SDV]Life Sciences [q-bio][ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingimage processingactive contours[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processingspraying application[SDE]Environmental Sciences[SDV.BV]Life Sciences [q-bio]/Vegetal Biologyhigh speed imaging[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingWeber number[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
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