Search results for "Pixel"

showing 10 items of 421 documents

Combining fuzzy C-mean and normalized convolution for cloud detection in IR images

2009

An important task for the cloud monitoring in several frameworks is providing maps of the cloud coverage. In this paper we present a method to detect cloudy pixels for images taken from ground by an infra-red camera. The method is a three-steps algorithm mainly based on a Fuzzy C-Mean clustering, that works on a feature space derived from the original image and the output of the reconstructed image obtained via normalized convolution. Experiments, run on several infra-red images acquired under different conditions, show that the cloud maps returned are satisfactory. © 2009 Springer Berlin Heidelberg.

Infra-red imagePixelSettore INF/01 - InformaticaComputer sciencebusiness.industryFeature vectorFuzzy setComputer Science (all)ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONCloud computingFuzzy logicImage (mathematics)Theoretical Computer ScienceNormalized convolutionComputer Science::Computer Vision and Pattern RecognitionFuzzy setComputer visionCloudiness maskArtificial intelligenceCluster analysisbusinessAstrophysics::Galaxy Astrophysics
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A non-parametric Scale-based Corner Detector

2008

This paper introduces a new Harris-affine corner detector algorithm, that does not need parameters to locate corners in images, given an observation scale. Standard detectors require to fine tune the values of parameters which strictly depend on the particular input image. A quantitative comparison between our implementation and a standard Harris-affine implementation provides good results, showing that the proposed methodology is robust and accurate. The benchmark consists of public images used in literature for feature detection.

Input imageContextual image classificationPixelSettore INF/01 - Informaticabusiness.industryCorner detectorFeature extractionDetectorIterative reconstructionImage segmentationNon-parametricFeature detectionEdge detectionStandard detectorsRobustness (computer science)Quantitative comparisonComputer visionArtificial intelligencebusinessMathematicsPublic image
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Orthoscopic long-focal-depth 3D integral imaging

2006

Integral imaging systems are imaging devices that provide 3D images of 3D objects. When integral imaging systems work in their standard configuration the provided reconstructed images are pseudoscopic; that is, are reversed in depth. In this paper we present, a technique for formation of real, undistorted, orthoscopic integral images by direct pickup. The technique is based on the use of a proper relay system and a global mapping of pixels of the elemental-images set. Simulated imaging experiments are presented to support our proposal.

Integral imagingOpticsPixelbusiness.industryComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONComputer visionPickupArtificial intelligenceIterative reconstructionbusinessDisplay deviceSPIE Proceedings
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Optimized integral imaging display by global pixel mapping

2006

Integral imaging systems are imaging devices that provide 3D images of 3D objects. When integral imaging systems work in their standard configuration the provided reconstructed images are pseudoscopic; that is, are reversed in depth. In this paper we present a technique for formation of real, undistorted, orthoscopic integral images by direct pickup. The technique is based on a global mapping of pixels of an elemental-images set. Simulated imaging experiments are presented.

Integral imagingPixelComputer sciencePixel mappingbusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImaging spectrometerIterative reconstructionSet (abstract data type)Computer graphics (images)Computer visionPickupArtificial intelligenceImage sensorbusinessSPIE Proceedings
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Elemental images for integral-imaging display

2013

One of the differences between the near-field integral imaging (NInI) and the far-field integral imaging (FInI), is the ratio between number of elemental images and number of pixels per elemental image. While in NInI the 3D information is codified in a small number of elemental images (with many pixels each), in FInI the information is codified in many elemental images (with only a few pixels each). The later codification is similar that the one needed for projecting the InI field onto a pixelated display when aimed to build an InI monitor. For this reason, the FInI cameras are specially adapted for capturing the InI field with display purposes. In this contribution we research the relation…

Integral imagingPixelComputer sciencebusiness.industryComputer graphics (images)Computer visionArtificial intelligenceProjection (set theory)businessSPIE Proceedings
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Gamma Knife treatment planning: MR brain tumor segmentation and volume measurement based on unsupervised Fuzzy C-Means clustering

2015

Nowadays, radiation treatment is beginning to intensively use MRI thanks to its greater ability to discriminate healthy and diseased soft-tissues. Leksell Gamma Knife® is a radio-surgical device, used to treat different brain lesions, which are often inaccessible for conventional surgery, such as benign or malignant tumors. Currently, the target to be treated with radiation therapy is contoured with slice-by-slice manual segmentation on MR datasets. This approach makes the segmentation procedure time consuming and operator-dependent. The repeatability of the tumor boundary delineation may be ensured only by using automatic or semiautomatic methods, supporting clinicians in the treatment pla…

Jaccard indexSimilarity (geometry)Computer scienceScale-space segmentationFuzzy logicunsupervised clusteringmagnetic resonance imagingSegmentationComputer visionmagnetic resonance imag- ingElectrical and Electronic EngineeringCluster analysisRadiation treatment planningSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniPixelbrain tumors; Gamma Knife treatment planning; magnetic resonance imaging; semi-automatic segmentation; unsupervised clusteringbusiness.industrybrain tumors Gamma Knife treatment planning magnetic resonance imaging semi-automatic segmentation unsupervised clusteringElectronic Optical and Magnetic Materialsbrain tumorsComputer Vision and Pattern RecognitionArtificial intelligencebusinesssemi-automatic segmentationSoftwarebrain tumorGamma Knife treatment planning
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Calibrating the effective scattering albedo in the SMOS algorithm: some first results

2016

International audience; This study focuses on the calibration of the effective scattering albedo (ω) of vegetation in the soil moisture (SM) retrieval at L-Band. Currently, in the SMOS Level 2 and 3 algorithms, the value of ω is set to 0 for low vegetation and ∼ 0.06 – 0.08 for forests. Different parameterizations of vegetation (in terms of ω values) were tested in this study. The possibility of combining soil roughness and vegetation contributions as a single parameter (“combined” method) leads to an important simplification in the algorithm and was also evaluated here. Following these assumptions, retrieved values of SMOS SM were compared with SM data measured over many in situ sites worl…

L band010504 meteorology & atmospheric sciencesPixelScattering0211 other engineering and technologies[SDU.STU]Sciences of the Universe [physics]/Earth SciencesSingle parameter02 engineering and technologyVegetationSMAP15. Life on landAlbedo01 natural sciencesscattering albedoCalibrationEnvironmental sciencesoil moistureL-MEB modelAlgorithmWater content[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingSMOS
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Monitoring global vegetation with the Yearly Land Cover Dynamics (YLCD) method

2011

Global vegetation has been traditionally monitored mainly through the use of the Normalized Difference Vegetation Index (NDVI). Land surface temperature (LST) provides additional information, and is generally less affected by atmospheric conditions when water vapor is taken into account. The Yearly Land Cover Dynamics (YLCD) method can then be used to retrieve 3 parameters which allow for a good differentiation between biomes at the global and local levels. Using NASA's Long Term Data Record (LTDR), the YLCD method has been applied to IDR (iterative Interpolation for Data Reconstruction) reconstructed LTDR data, in order to account for atmospheric contamination of part of the dataset for a …

Land surface temperaturePixelBiomeEnvironmental scienceLand coverVegetationNormalized Difference Vegetation IndexWater vaporRemote sensingInterpolation2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)
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MuPix and ATLASPix -- Architectures and Results

2020

High Voltage Monolithic Active Pixel Sensors (HV-MAPS) are based on a commercial High Voltage CMOS process and collect charge by drift inside a reversely biased diode. HV-MAPS represent a promising technology for future pixel tracking detectors. Two recent developments are presented. The MuPix has a continuous readout and is being developed for the Mu3e experiment whereas the ATLASPix is being developed for LHC applications with a triggered readout. Both variants have a fully monolithic design including state machines, clock circuitries and serial drivers. Several prototypes and design variants were characterised in the lab and in testbeam campaigns to measure efficiencies, noise, time reso…

Large Hadron ColliderFinite-state machinePhysics - Instrumentation and DetectorsPixelComputer scienceDetectorFOS: Physical sciencesHigh voltageInstrumentation and Detectors (physics.ins-det)Tracking (particle physics)7. Clean energyNoise (electronics)Electronic engineeringDetectors and Experimental Techniquesddc:620physics.ins-detEngineering & allied operationsDiode
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Architecture-Driven Level Set Optimization: From Clustering to Sub-pixel Image Segmentation

2016

Thanks to their effectiveness, active contour models (ACMs) are of great interest for computer vision scientists. The level set methods (LSMs) refer to the class of geometric active contours. Comparing with the other ACMs, in addition to subpixel accuracy, it has the intrinsic ability to automatically handle topological changes. Nevertheless, the LSMs are computationally expensive. A solution for their time consumption problem can be hardware acceleration using some massively parallel devices such as graphics processing units (GPUs). But the question is: which accuracy can we reach while still maintaining an adequate algorithm to massively parallel architecture? In this paper, we attempt to…

Level set methodComputer science0211 other engineering and technologiesInitialization02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingLevel setgraphics processing units0202 electrical engineering electronic engineering information engineeringLevel set methodComputer visionElectrical and Electronic EngineeringCluster analysisMassively parallelimage segmentation021101 geological & geomatics engineeringActive contour modelhybrid CPU-GPU architecturebusiness.industryImage segmentationSubpixel renderingComputer Science ApplicationsHuman-Computer InteractionControl and Systems EngineeringHardware acceleration020201 artificial intelligence & image processingArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingSoftwareInformation Systems
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