Search results for "Image formation"

showing 10 items of 24 documents

Image Processing Techniques For the Detection and Classification of Man Made Objects in Side-Scan Sonar Images

2011

This is a review chapter that surveys past work in, and the recent status of image processing and other related techniques involved in the detection and classification of man made objects in side scan sonar images. Side scan sonar is a readily, available and cheap device which has found increasing applications, specially for military purposes such as Computer Aided Detection (CAD) and Classification (CAC) of mines. Therefore the main focus of the chapter is on this topic. The list of references is sufficiently complete to include most past and recent publications in the open refereed literature. Although side scan sonar displays many features similar to an optical sensor from a purely image…

Image formationSide-scan sonarbusiness.industryDigital image processingProcess (computing)Computer visionImage processingNoise (video)Artificial intelligenceFocus (optics)businessSonar
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Software for automated application of a reference-based method fora posterioridetermination of the effective radiographic imaging geometry

2005

Objectives: Presentation and validation of software developed for automated and accurate application of a reference-based algorithm (reference sphere method: RSM) inferring the effective imaging geometry from quantitative radiographic image analysis. Methods: The software uses modern pattern recognition and computer vision algorithms adapted for the particular application of automated detection of the reference sphere shadows (ellipses) with subpixel accuracy. It applies the RSM algorithm to the shadows detected, thereby providing threedimensional Cartesian coordinates of the spheres. If the three sphere centres do not lie on one line, they uniquely determine the imaging geometry. Accuracy …

Image formationSoftware ValidationImage registrationGeometryMandibleEllipsePattern Recognition Automatedlaw.inventionImaging Three-DimensionalSoftwareArtificial IntelligenceSoftware DesignlawImage Processing Computer-AssistedHumansSuperimpositionRadiology Nuclear Medicine and imagingCartesian coordinate systemGeneral DentistryMathematicsbusiness.industryRadiography Dental DigitalGeneral MedicineSubpixel renderingOtorhinolaryngologyCalibrationLine (geometry)businessAlgorithmsSoftwareDentomaxillofacial Radiology
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Modelling spatial and spectral systematic noise patterns on CHRIS/PROBA hyperspectral data

2006

In addition to typical random noise, remote sensing hyperspectral images are generally affected by non-periodic partially deterministic disturbance patterns due to the image formation process and characterized by a high degree of spatial and spectral coherence. This paper presents a new technique that faces the problem of removing the spatial coherent noise known as vertical stripping (VS) usually found in images acquired by push-broom sensors, in particular for the Compact High Resolution Imaging Spectrometer (CHRIS). The correction is based on the hypothesis that the vertical disturbance presents higher spatial frequencies than the surface radiance. The proposed method introduces a way to…

Image formationSpectrometerComputer scienceNoise reductionRadianceHyperspectral imagingSpatial frequencySpectral resolutionRadiometric calibrationRemote sensing
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Analysis of image formation with a photon scanning tunneling microscope

1996

International audience; The photon scanning tunneling microscope (PSTM) is based on the frustration of a total internal reflected beam by the end of an optical fiber. Until now it has been used to obtain topographic information, generally for smooth samples. We report theoretical as well as experimental results on the observation of a step on a quartz substrate with the PSTM. These results demonstrate the effects on image formation of the distance between the fiber tip and the sample surface, the orientation of the incident beam with respect to the step, the polarization, and the coherence of the light. Good agreement exists between numerical simulations and experiments. We show that a pert…

Image formation[PHYS.PHYS.PHYS-OPTICS] Physics [physics]/Physics [physics]/Optics [physics.optics]Optical fiberScanning tunneling spectroscopy02 engineering and technology01 natural scienceslaw.invention010309 opticsScanning probe microscopyOpticslaw0103 physical sciencesLight beamPhysicsTotal internal reflection[PHYS.PHYS.PHYS-OPTICS]Physics [physics]/Physics [physics]/Optics [physics.optics][ PHYS.PHYS.PHYS-OPTICS ] Physics [physics]/Physics [physics]/Optics [physics.optics]business.industryNear-field optics021001 nanoscience & nanotechnologyAtomic and Molecular Physics and OpticsElectronic Optical and Magnetic MaterialsComputer Vision and Pattern RecognitionScanning tunneling microscopebusiness0210 nano-technology
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Strain state analysis of InGaN/GaN - sources of error and optimized imaging conditions.

2006

Transmission electron microscopy investigation of the chemical composition of In x Ga 1-x N/GaN layers by strain state analysis can lead to substantial artefacts. We evaluated simulated images in dependence of specimen thickness, specimen orientation and objective lens defocus. We observed that the measurement is in agreement with the true strain profile for certain conditions only. An analysis of error sources revealed that artefacts are mainly caused by a combination of delocalization and the composition dependence of the phases of the beams contributing to the image formation. The delocalization effect is minimized for interference of the undiffracted beam with one of the 000 ± 2 beams. …

Image formationbusiness.industryChemistrySurfaces and InterfacesElasticity (physics)Condensed Matter PhysicsMolecular physicsSurfaces Coatings and FilmsElectronic Optical and Magnetic MaterialsOpticsTransmission electron microscopyFinite strain theory[ CHIM.MATE ] Chemical Sciences/Material chemistryMicroscopyLattice planeMaterials ChemistryElectrical and Electronic EngineeringbusinessBeam (structure)Plane stress
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Correction of systematic spatial noise in push-broom hyperspectral sensors: application to CHRIS/PROBA images

2008

Hyperspectral remote sensing images are affected by different types of noise. In addition to typical random noise, nonperiodic partially deterministic disturbance patterns generally appear in the data. These patterns, which are intrinsic to the image formation process, are characterized by a high degree of spatial and spectral coherence. We present a new technique that faces the problem of removing the spatially coherent noise known as vertical striping, usually found in images acquired by push-broom sensors. The developed methodology is tested on data acquired by the Compact High Resolution Imaging Spectrometer (CHRIS) onboard the Project for On-board Autonomy (PROBA) orbital platform, whi…

Image formationmedicine.medical_specialtySpectrometerbusiness.industryComputer scienceMaterials Science (miscellaneous)Noise reductionHyperspectral imagingSpectral density550 - Earth sciencesImage processingIndustrial and Manufacturing EngineeringSpectral imagingNoiseOpticsmedicineImage noiseSpatial frequencyBusiness and International ManagementbusinessRemote sensingApplied Optics
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Analysis of photon-scanning tunneling microscope images of inhomogeneous samples: determination of the local refractive index of channel waveguides

1995

Channel waveguides are imaged by a photon-scanning tunneling microscope (PSTM). The polarization of the light and its orientation with respect to the guide axis are shown to be very important parameters in the analysis of the images of such samples. We simulated image formation for the plane of incidence parallel to the axis of the guide. Our theoretical results are qualitatively in agreement with our measurements. These results show the ability of the PSTM to give information about the local refractive-index variations of a sample.

PhysicsImage formationTotal internal reflectionPlane of incidencebusiness.industryPhysics::OpticsPolarization (waves)Atomic and Molecular Physics and OpticsElectronic Optical and Magnetic Materialslaw.inventionLight intensityOpticslawLight beamComputer Vision and Pattern RecognitionScanning tunneling microscopebusinessRefractive indexJournal of the Optical Society of America A
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Photorefractive “camera obscura”

2011

Abstract We demonstrate a novel scheme for lensless image formation which combines the properties of an amplifying dynamic hologram and a pinhole camera. The scheme is realized on the base of a SPS:Sb1% photorefractive crystal working at 633 nm.

PhysicsImage formationbusiness.industryHolographyPhotorefractive effectPhysical opticsAtomic and Molecular Physics and OpticsElectronic Optical and Magnetic Materialslaw.inventionOpticslawPhotorefractive crystalPinhole cameraOptoelectronicsElectrical and Electronic EngineeringPhysical and Theoretical ChemistrybusinessCamera obscuraOptics Communications
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A computer-assisted experiment to study the influence of the point spread function in the image formation process

2018

[EN] We present a new open experimental setup assisted with LabView to be used to teach the concept of the point spread function (PSF). The PSF describes the response of an image-forming system to a point object. The PSF concept is of fundamental importance in optics since the output of an image-forming system can be simulated as the convolution of the PSF with the input object. In this work, a new graphical user interface has been developed to obtain a real-time measure of the PSF and the corresponding images provided by different lenses and pupils with different sizes and shapes. From a didactical point of view, the proposed method allows students to interpret the results in a visual and …

PhysicsPoint spread functionImage formation05 social sciencesProcess (computing)ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION050301 educationGeneral Physics and Astronomy01 natural sciences010309 opticsImage formationComputer graphics (images)FISICA APLICADA0103 physical sciencesGUIMATEMATICA APLICADA0503 educationPSF
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Total Variation Based Image Restoration

2004

For the purpose of image restoration the process of image formation can be modeled in a first approximation by the formula [207] $$ {u_d} = Q\{ II(k*u) + n\} , $$ (1.1) where u represents the photonic flux k is the point spread function of the optical-captor joint apparatus П is a sampling operator, i.e., a Dirac comb supported by the centers of the matrix of digital sensors, n represents a random perturbation due to photonic or electronic noise, and Qis a uniform quantization operator mapping ℝ to a discrete interval of values, typically [0, 255].

Point spread functionImage formationPhysicsDiscrete mathematicssymbols.namesakeMatrix (mathematics)Sampling (signal processing)Operator (physics)symbolsInterval (mathematics)Dirac combImage restoration
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