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60 results for “Depth Image”
Sidescan Sonar Substrate, Depth, and Shadow Image-Label-Pairs
<p>Substrate, depth, and shadow image-label pairs used to train side scan sonar segmentation models v1.0 implemented in PINGMapper v2.0.</p><p> </p><p>Images were labeled with <a href="https://github.com/Doodleverse/dash_doodler">Doodler</a> and <a href="https://www.makesense.ai/">Make Sense.</a></p>
Dataset for the publication "Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site"
<p>This datasset contains data to reproduce the following figures of the paper <em>Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site</em>:</p> <ul> <li> <p>Time series data of Figures 1c and 2</p> </li> <li> <p>Data (*.asc) used for plotting Figures 1d and 1e (as well as Figure S3 and S4)</p> </li> <li>Pléiades snow depth map (Figure S1)</li> <li> <p>Data used for plotting Figure S2</p> </li> </ul> <p> </p>
Snow depth and land surface cover in Tuolumne basin (California) from Pléiades images
<p>This dataset contains products calculated from Pléiades images.</p> <p>Details about the products are available in https://doi.org/10.5194/tc-2020-15.</p> <p>These products were used in Figure 4.</p> <p>- pleiades_elevation_difference_raw_winter_minus_summer.tif : raw difference of digital elevation models (DEMs) calculated from Pléiades stereo images.</p> <p>- pleiades_snow_depth_winter.tif : difference of DEMs on snow terrain only (where pleiades_land_surface_cover_winter.tif==1 with morphological erosion)</p> <p>- pleiades_land_surface_cover_winter.tif : land cover surface in the winter images (1= snow, 2=forest, 3= stable terrain, 4=water)</p> <p>- pleiades_land_surface_cover_summer.tif : land cover surface in the summer images (1= snow, 2=forest, 3= stable terrain, 4=water) </p> <p>- elevation_difference_style.qml : qgis style used for elevation difference and snow depth.</p> <p>- land_surface_cover_style.qml : qgis style used for land cover surface.</p> <p> </p>
Research data supporting "Raman spectroscopic imaging for quantification of depth-dependent and local heterogeneities in native and engineered cartilage"
<p>Research data supporting the publication: Albro M. et al., 2018, npj Regenerative Medicine, DOI: https://doi.org/10.1038/s41536-018-0042-7.</p>
Dataset for image segmentation of tree trunks from depth maps captured with a an Android app using Google ARCore
<p>This dataset consists of pairs of depth maps created with a custom-built Android app using Google's ARCore for depth estimates, and tree trunk segments obtained from depth maps captured by Huawei''s AREngine and processed with a tree diameter estimation algorithm. This dataset was used for a machine learning segmentation task that aimed to improve the inputs from ARCore tree trunk depths to the tree diameter estimation algorithm. For each pair of depth maps and segments an RGB image of the scene where samples were captured is also included.</p>
Global daily Aerosol Optical Depth measurements from Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Aqua and Terra satellites
<p>This repository contains input MODIS AOD data prepared for “Subways and Urban Air Polution” by Gendron-Carrier, Gonzalez-Navarro, Polloni and Turner (American Economic Journal: Applied Economics, <a href="https://doi.org/10.1257/app.20180168">https://doi.org/10.1257/app.20180168</a>). The main replication archive is available at <a href="https://doi.org/10.3886/E126401V1">https://doi.org/10.3886/E126401V1</a> .</p> <p>Description of input MODIS AOD data</p> <p>The Moderate Resolution Imaging Spectroradiometers aboard the Terra and Aqua earth-observing satellites provide daily measures of the aerosol optical depth of the atmosphere at a 3km spatial resolution everywhere in the world. Data is available in ‘granules’ which describe five minutes of satellite time. These granules are available, more or less continuously, from February 24, 2000 for the Terra satellite and from July 4, 2002 for Aqua. During September of 2018, we downloaded all available granules for Terra and Aqua until August 31, 2018 and subsequently consolidated them into daily rasters describing global AOD. In August 2020, we processed additional Terra data. This archive therefore contains daily rasters for Aqua (from 2002-07-04 to 2018-08-31) and Terra (from 2000-02-24 to 2020-07-31). We note that February 2005 data are missing for the Aqua satellite.</p> <p> </p> <p>We use source products MOD04_3K (<a href="https://doi.org/10.5067/MODIS/MOD04_L2.006">https://doi.org/10.5067/MODIS/MOD04_L2.006</a>) and MYD04_3K (<a href="https://doi.org/10.5067/MODIS/MYD04_L2.006">https://doi.org/10.5067/MODIS/MYD04_L2.006</a>). The product files are stored in Hierarchical Data Format (HDF) and we use the "Optical Depth Land And Ocean" layer, which is stored as a Scientific Data Set (SDS) within the HDF file, as our measure of aerosol optical depth. The "Optical Depth Land And Ocean" dataset contains only the AOD retrievals of high quality. We convert all HDF formatted granules to GIS compatible formats using the HDF-EOS To GeoTIFF Conversion Tool (HEG) provided by NASA’s Earth Observing System Program. We consolidate GeoTIFF granules into a global raster for each day using ArcGIS. First, we keep only AOD values that do contain information. The missing value is -9999 in AOD retrievals. Second, we create a raster catalog with all the granules for a given day and calculate the average AOD value using the Raster Catalog to Raster Dataset tool. The code used to accomplish this is included for reference purposes in “dofiles/old_work” of the main replication archive at <a href="https://doi.org/10.3886/E126401V1">https://doi.org/10.3886/E126401V1</a>.</p>
Co-manipulation of soft-materials estimating deformation from depth images
<p>Video describing our work on Co-manipulation of soft-materials estimating deformation from depth images</p>
Co-manipulation of soft-materials estimating deformation from depth images [Dataset]
<p>The dataset and training results used in the paper "Co-manipulation of soft-materials estimating deformation from depth images" G. Nicola, E. Villagrossi, N.Pedrocchi submitted at Robotics and Computer Integrated Manufacturing.</p>
Kirchhoff pre-stack depth migration images of the multi-channel seismic data, SO190, RV. SONNE
<p>The dataset consists of four newly processed 2-D pre-stack depth migrated multi-channel seismic lines (BGR06_303, BGR06_305, BGR06_311 and BGR06_313) collected by GEOMAR and BGR in 2006. The dataset reveals the subducted oceanic reliefs and detailed accretionary wedge structure offshore eastern Java, Bali, Lombok, and Sumbawa islands, along the Sunda arc. The dataset is saved in standard SEGY format and could be loaded in open-source or commercial software. </p>
Depth image-based deformation estimation of deformable objects for collaborative mobile transportation
<p>Human-Robot collaborative transportation is a promising technology that combines the strength of humans and robots.<br> The most common approaches rely on methodologies that exploit force-sensing. However, the drawbacks are multiple. First, the magnitude of force applied might be limited to avoid damages. Then, force measurements might be unidirectional according to the material properties; e.g., compression forces are not measurable for fabrics.<br> This paper proposes an approach based on estimating the deformation state of the manipulated object from depth images. Specifically, the segmented depth images of the manipulated object are fed to a Convolutional Neural Network (CNN) model to estimate the current deformation status. Compared with the desired deformation, the current deformation status is used to generate the robot's twist command. The methodology is proved in a mobile robot application, where carbon-fiber fabrics are transported. A comparison with the state-of-the-art is reported proving that the proposed method is more accurate and more repeatable.</p>
Supplementary material 2 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
SV1: EDOF imaging : Explanation note: This video demonstrates the effect of the registered EDOF-calculation.
Supplementary material 1 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Technical information : Explanation note: Detailed technical information and additional theoretical background.
Supplementary material 3 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
SV2: Illustrative examples : Explanation note: Illustrative examples of insects and snail shell models generated with DISC3D.
Quantitative measures of corneal transparency, derived from objective analysis of depth-resolved corneal images, demonstrated with full-field optical coherence tomographic microscopy
<p>Supporting data for: <a href="https://zenodo.org/record/2579947">Quantitative measures of corneal transparency, derived from objective analysis of depth-resolved corneal images, demonstrated with full-field optical coherence tomographic microscopy</a></p>
Magnifying Endoscopy With Narrow Band Imaging Versus Endoscopic Ultrasonography for Prediction of Tumor Invasion Depth in Early Gastric Cancer: A Prospective Comparative Study
ClinicalTrials.gov study NCT03546257. IPD Sharing: NO. Countries: 0. Publications: 14.
Thoracic Epidural Depth in Children Using Magnetic Resonance Imaging.Magnetic Resonance Imaging
ClinicalTrials.gov study NCT02785081. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Effects of Music and Maternal Voice on Sedation Depth and Sedative Use During Pediatric Magnetic Resonance Imaging.
ClinicalTrials.gov study NCT04944875. IPD Sharing: YES. Countries: 1. Publications: 2.
Data from: Depth image super-resolution reconstruction based on a modified joint trilateral filter
Depth image super-resolution (SR) is a technique that utilizes signal processing technology to enhance the resolution of a low-resolution (LR) depth image. Generally, the external database or high-resolution (HR) images are needed for acquiring the priori information to support the SR reconstruction. To overcome the limitation, a depth image SR method which does not need the reference of any external images is proposed. In the paper, a high-quality edge map is firstly constructed using a sparse coding method, which uses a dictionary learned from the original images themselves at different scales. Then, the high-quality edge map is used to guide the interpolation for depth images by a modified joint trilateral filter. During the interpolation, some information of gradient and structural similarity (SSIM) are added to preserve the detailed information and suppress the noise. The proposed method not only can preserve the sharpness of image edge, but also can avoid the dependence on database. Experimental results show the proposed method is superior to some state-of-the-art depth image SR methods.
Data from: Retrospective study using magnetic resonance imaging (MRI) to measure depths of acupuncture points in neck and shoulder region
Objectives: There are safety issues associated with acupuncture treatment. Previous studies regarding needling depth of acupuncture points revealed inconsistent results due to vague depth definition, acupuncture point localisation and measuring tools. The objective of this study is to find and compare the differences of the mean depths of 11 acupuncture points in the neck and shoulder region between subjects, with variables including gender and body mass index (BMI). Setting: This study was conducted at a single medical center in Taiwan. Participants: Three hundred and ninety-four participants were included in this study. Participants were grouped according to gender and BMI. Acupuncture points were localised by WHO standard and measured by MRI. Outcome measures: The distance from the needle insertion point (surface of the skin) to any tissues that would cause possible/severe complications. Results: Mean depths of 11 points were obtained in groups of different BMI and gender. Mean depths of all participants regardless of BMI and gender are as follows, in centimetres: GB21=5.6, SI14=5.2, SI15=8.8, GV15=4.9, GV16=4.6, GB20=5.0, ST9=1.6, SI16=1.8, SI17=2.4, TE16=3.1, LI18=1.3. Participants with higher BMI had greater measured depths in both gender groups. Male participants had larger mean depths than female participants regardless of BMI except in SI17 and LI18. When taking BMI into consideration, depths in male participants are greater than in female participants in most of the points except the following: GB21, TE16 in obesity group; ST9 in underweight and obesity group; SI16 in ideal body weight, overweight and obesity group; SI17, LI18 in each group. Conclusions: Participants with higher BMI had greater measured depths and males tended to have greater depths in most of the points. Clinical practitioners are recommended to consider this information to prevent complications when applying acupuncture treatment to their patients.
Figure 1 from: Huntenburg J, Wagstyl K, Steele C, Funck T, Bethlehem R, Foubet O, Larrat B, Borrell V, Bazin P (2017) Laminar Python: tools for cortical depth-resolved analysis of high-resolution brain imaging data in Python. Research Ideas and Outcomes 3: e12346. https://doi.org/10.3897/rio.3.e12346
Figure 1 - Laminar python pipeline, demonstrated using high-resolution MR data of a ferret brain. a) Binary images demarcating inner (grey-white matter interface, top) and outer (pial surface, bottom) boundaries of the cortex. b) Levelset representations of the same surfaces, where positive values are assigned to voxels outside of the volume deliminated by the surface, and negative values to voxels inside, each increasing in value with euclidean distance from the surface. c) Continuous equivolumetric intracortical depth, which models the positions of laminae relative to cortical morphology. d) Discrete representations of equivolumetric depth levels. e) T2 values, sampled at the six equivolumetric intracortical depths. Note that the equivolumetric laminae do not represent architectonic layers, but provide an anatomically meaningful coordinate system of cortical depth.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.