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164 results for “image quality”
A Dataset Containing Tiny and Low Quality Images for Vehicle Classification
<p>This dataset contains 4800 tiny and low resolution vehicle images. The vehicles in the images are grouped in six classes: Bike, Car, Juggernaut, Minibus, Pickup, and Truck. For each class, there are 800 vehicle images with 100 × 100 pixels and 96 dpi resolution.</p> <p><strong>The peer-reviewed research article for this dataset has been published in MDPI Sensors, and can be accessed here: <a href="https://doi.org/10.3390/s22134740">https://doi.org/10.3390/s22134740</a>. Please cite this when using the dataset.</strong></p>
Dataset - No Reference Image Quality assessment Scores for Humanities Online Repositories
<p>The dataset contains data on No-Reference Image Quality Assessment (NR-IQA) scores for online repositories in the humanities.</p>
Processing steps to generate a Digital Surface Model based on SPOT-7 tri-stereo images published in the study "An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar inundation areas at volcan Copahue (Argentina & Chile)" in the Journal of South American Earth Sciences https://doi.org/10.1016/j.jsames.2022.104138
<p>The Digital Surface Model (DSM) was created from SPOT-7 tri-stereo images for the Copahue volcano between the border of Argentina and Chile. Two versions of the DSM are provided: an unfiltered product and a final, filtered product. The final product has a spatial resolution of 5-m and was used for lahar inundation modeling for the Copahue volcano (Viotto, Toyos, and Bookhagen 2022, <a href="https://doi.org/10.1016/j.jsames.2022.104138">https://doi.org/10.1016/j.jsames.2022.104138</a> : An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at Volcán Copahue (Argentina & Chile). <em>Journal of South American Earth Sciences</em> ). The dataset provided should be cited together with the article. </p> <p><strong>DSM processing </strong></p> <p>The source images were given by a SPOT-7 snow- and cloud-free triplet (Nadir, Backward and Forward) of 1.5 m spatial resolution from 19 April 2018 (SPOT Image, Airbus Defence and Space GmbH, distributed by CONAE; Dataset ID: <em>SEN_SPOT7_20180419_142955500_000</em>, delivered by CONAE as <em>DS_SPOT7_20180419</em>).</p> <p>The data were processed with the suite of digital photogrammetry tools AMES Stereo Pipeline ASP (Beyer et al., 2018). The procedure for the generation of the DSM is summarized by following steps: </p> <ol> <li> <p>The orbital parameters (RCP models) were adjusted using the bundle adjustment tool with no ground control points, since they were unavailable.</p> </li> <li> <p>The scenes were map-projected onto the NASADEM (spatial resolution of 30 m) elevation dataset, assisted by the results of the orbital adjustment in Step 1.</p> </li> <li>The stereo correlation of the map-projected scenes including the results of the adjusted orbital parameters, was performed three times, using as first scene (i.e., primary image) the nadir (N), backward (B), and forward (F) images . In each run, the order of images to perform the stereo correlation was: N-F-B, F-N-B, and B-N-F. Thus, three point clouds were generated. Specific ASP correlator settings (other than defaults parameters; for details see the provided stereo-default file) were set in the following way: <em>Correlation Kernel</em>: 15 x 15 pixels; <em>Sub-pixel Refinement Kernel</em>: 21 x 21 pixels; <em>Subpixel Refinement Mode</em>: 2 (Weighted Affine Adaptive Window Correlator EM)</li> <li> <p>The three point clouds were merged into one point cloud with a regular grid of 5 m (unfiltered product, known as <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em>).</p> </li> </ol> <p>The quality of the final point cloud was assessed by comparing the unfiltered DSM with a spatial resolution of 12-m against the WorldDEM<sup>TM</sup> elevation dataset (Collins et al., 2015). The WorldDEM was provided by Airbus Defence and Space GmbH under license for the scope of the Viotto et al., 2022 study. The comparison of the pixel-to-pixel heights above the ellipsoid (WGS84) between the two datasets resulted in a mean difference of 0.67 m and a standard deviation of +/- 4.82 m. </p> <p>Comprehensive details on the methodologies evaluated to create the dataset with ASP, can be found in the corresponding master's thesis “Topografía digital y modelado de lahares en el Volcán Copahue, Argentina-Chile” from S. Viotto (link: https://rdu.unc.edu.ar/handle/11086/15384). Recommended literature about processing DEMs from SPOT imagery is given by Mueting et al., 2021 (<a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330</a>). </p> <p><strong>Creation of the Final, Filtered DSM product</strong></p> <p>The corrections and improvements applied to the unfiltered product to create the final, filtered DSM (named DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif) are summarized by following steps. </p> <p> </p> <ol> <li> <p><em>Water Bodies Delineation</em></p> </li> </ol> <p>The delineation of the water bodies was based on a mask created from the free access water bodies datasets provided by the Instituto Geográfico Nacional of Argentina (<a href="https://www.ign.gob.ar/NuestrasActividades/InformacionGeoespacial/CapasSIG">https://www.ign.gob.ar/ NuestrasActividades/InformacionGeoespacia l/CapasSIG</a>) and by the Ministerio de Bienes Nacionales in Chile ( <a href="https://www.ide.cl/index.php/aguas-continentales/item/1508-catastro-de-lagos">https://www.ide.cl/index.php /aguas-continentales/item/1508-catastro-de-lagos</a>). A total of 45 lakes within the area of interest were considered. Lakes with areas below or equal to 25 m2 were smoothed with a median filter in the last step. Lakes with areas above this threshold were filled in with a constant value and their borders were smoothed with a median filter to provide smooth shorelines.</p> <p><em>2 . Void Filling</em></p> <p>Voids (other than water bodies) were filled with the tool “Close Gaps” from Saga GIS software. </p> <p><em>3. Smoothing</em></p> <p>Finally, the elevation dataset was smoothed with a median filter using a 3 x 3 pixel window, excluding water bodies filled in the step 1. </p> <p><strong>Final Remarks and Suggestion</strong></p> <p>The quality assessment of the final version by visual inspection of the hillshades suggested an improvement of the signal to noise ratio. However, the void filling process may be improved.</p> <p><br> </p> <p><strong>Dataset Description</strong></p> <table align="center"> <caption> </caption> <tbody> <tr> <td>Digital Surface Models</td> <td> <p>No Data Value = -9999</p> <p>Format = float 32 bit</p> <p>File Format = GeoTiff</p> <p>Vertical Datum: WGS84</p> <p>Projection information: EPSG 32719 (UTM19S)</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>) </p> <p>Versions: </p> <ul> <li> <p>Unfiltered product: without corrections <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em></p> </li> <li> <p>Final, filtered product: smoothed and void filled <em>DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</em></p> </li> </ul> </td> </tr> <tr> <td>Water Bodies Mask</td> <td> <p>No Lake Value = 0</p> <p>Lakes Values = 1 to 45</p> <p>File Format= GeoTiff</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>)</p> <p>Projection information : EPSG 32719 (UTM19S)</p> <p><em>WB_mask_5m_UTM19S.tif</em></p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><strong>Repository structure</strong></p> <p>|__ 01_Scripts</p> <p> |+ run21_CopahueDSM_AMES_sviotto.sh</p> <p> |+ stereo.default</p> <p>|__ 02_DSMs</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_raw.tif</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</p> <p> |+ WB_mask_5m_UTM19S.tif</p> <p><strong>References</strong></p> <p>Beyer, R. A., Alexandrov, O., & McMichael, S. (2018). The Ames Stereo Pipeline: NASA's open source software for deriving and processing terrain data. <em>Earth and Space Science</em>, 5, 537– 548. <a href="https://doi.org/10.1029/2018EA000409">https://doi.org/10.1029/2018EA000409</a></p> <p>Collins, J., Riegler, G., Schrader, H., Tinz, M., 2015. Applying terrain and hydrological editing to TanDEM-X data to create a consumer-ready worlddem product. Int. Arch. Photogram. Rem. Sens. Spatial Inf. Sci. 40 (7), 1149. https://doi.org/10.5194/isprsarchives-XL-7-W3-1149-2015.</p> <p>Mueting, A., Bookhagen, B., & Strecker, M. R. (2021). Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina. <em>Journal of Geophysical Research: Earth Surface</em>, 126, e2021JF006330. <a href="https://doi.org/10.1029/2021JF006330">https://doi.org/10.1029/2021JF006330</a></p> <p>Viotto, S., Toyos, G., & Bookhagen, B. (2022). An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at volcán copahue (Argentina & Chile). Journal of South American Earth Sciences, 104138. https://doi.org/10.1016/j.jsames.2022.104138</p> <p> </p> <p> </p>
Beam profiles images measured of an undulator- produced X-ray beam reflected by a high-quality toroidal mirror
<p>The experimental data have been collected at the ID09 ESRF beamline at the EBS-ESRF storage ring, we used an IVU17 undulator set to have its first harmonic at an energy of E = 18.5 keV. The toroidal mirror have adjustable meridional radius, with 0.5 μrad rms of slope error, 18 nm rms of height error and a micro-roughness of 2.8 Å. Each h5 file contains results from different scan measurements, logbook, Python scripts data analysis and OASYS workflows for simulations are allocated in a GitHub repository: <a href="https://github.com/jureyherrera/toroidal_mirror_project">https://github.com/jureyherrera/toroidal_mirror_project</a></p>
image classification dataset on tailored textiles quality control
<p>This dataset was geared towards representing practical quality control scenarios, specifically involving the quality inspection of glass fiber fabric. Continuous rolls of glass fiber fabric were cut into samples of 300x200 mm. Half of these samples were reinforced with a single carbon fiber. These samples were then classified into six different categories based on the presence of common defects or if they were error-free textiles. Each category consists of 300 images, with a resolution of 4288x2848 pixels.</p>
RADIQAL Study (Radiation Dose and Image Quality Trial)
ClinicalTrials.gov study NCT06944509. IPD Sharing: NO. Countries: 4. Publications: 6.
Effects of Contrast Media Temperature on Image Quality and Clinical Adverse Events in Coronary CTA
ClinicalTrials.gov study NCT05489055. IPD Sharing: NO. Countries: 1. Publications: 3.
High-quality Image (NIR and RGB) Dataset Synchronized With Contact Vital Sings Recordings and Clinical Data of Stratified Healthy Population. Algorithms and AI Models to Obtain a Set of Vital Signs Im
ClinicalTrials.gov study NCT05947721. IPD Sharing: NO. Countries: 1. Publications: 2.
Assessing Environmental Factors in Healthcare Facilities in Order to Improve the Experience of Patients, Staff, and the Quality of Imaging Procedures
ClinicalTrials.gov study NCT03456895. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Improved Image Quality for Assessment of Carotid Artery Stenosis by Ultrafast Ultrasound FLOW Imaging
ClinicalTrials.gov study NCT06170580. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
The Impact of Different Scanning Methods and Reconstruction Algorithms on CT Image Quality
ClinicalTrials.gov study NCT06142539. IPD Sharing: NO. Countries: 1. Publications: 1.
Improving Quality of Colonoscopy Using a 3D-imager
ClinicalTrials.gov study NCT00519129. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Non-interventional Study to Observe the Computed Tomographic Angiography Image Quality With Different Contrast Media Injection Protocols Under Different Computed Tomography Machines Parameters Setti
ClinicalTrials.gov study NCT02840903. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effects of Heart Rates and Variability of Heart Rates on Image Quality of Dual-Source CT Coronary Angiography
ClinicalTrials.gov study NCT00632918. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Impact of Reconstruction Method (ASIR, FBP) Used in CT on Bone SPECT/CT Image Quality
ClinicalTrials.gov study NCT01800084. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Image Quality of EOSedge for Radiographic Evaluation of Hip Implant
ClinicalTrials.gov study NCT05615701. IPD Sharing: Not stated. Countries: 1. Publications: 0.
99mTc-MIP-1404 for Imaging Prostate Cancer: Phase I Clinical Study to Assess the Image Quality of a Simplified Kit Formulation Compared to a Multi-step Preparation of 99mTc-MIP-1404
ClinicalTrials.gov study NCT01654874. IPD Sharing: Not stated. Countries: 1. Publications: 14.
Comparison of Image Quality of Coronary Computed Tomography Angiography bEtweeN High concenTRATion and Low concEntration Contrast Agents(CONCENTRATE Trial)
ClinicalTrials.gov study NCT02549794. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Registry for Quality Assessment With Ultrasound Imaging and TTFM in Cardiac Bypass Surgery
ClinicalTrials.gov study NCT02385344. IPD Sharing: Not stated. Countries: 6. Publications: 3.
Totally Implantable Venous Access Devices: Quality of Life and Body Image
ClinicalTrials.gov study NCT02075580. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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OpenNeuro
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