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438 results for “3D imaging”
Fig. 8.2 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 8.2. Annotated model of a grass snake skull on Sketchfab. https://skfb.ly/6tNwY
Fig. 6.3 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 6.3. Back view of the skull digitised with different scanners.
Fig. 6.4 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 6.4. Parietal view of the skull digitised with different scanners.
Fig. 6.5 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 6.5. Sea urchin captured by 3 different structured light scanners and by SfM.
Fig. 5.5 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 5.5. Corrected light settings (right image) for a too-dark texture (left image).
Fig. 8.3 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 8.3. Virtual collections page of RBINS
Fig. 3.5 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 3.5. Interface of the AGORA 3D Automated Photographer.
Fig. 3.23 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 3.23. The HDI white light scanner.
Fig. 3.22 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 3.22. Fringe projection.
Fig. 3.24 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 3.24. The MechScan white light scanner.
Fig. 2.27 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 2.27. Ammonite lighted with the RTI technique. Left normal view, right specular view.
Fig. 2.16 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 2.16. Caloptilia galacotra, detail of the reproductive organs at 20× magnification.
Fig. 2.21 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 2.21. Overview of the specimen page visible at Zoosphere.net. Image copyright MfN.
Pore-scale 3D dynamic morphological modeling and physical characterization of hydrate-bearing sediment based on computed tomography images
<p>This supporting information includes one Figure-S1-REV300, which is the representative elemental volume (REV) used in this study. And the Figure-S1-REV300 is 300*300*300 with a voxel size of 4.4 μm. There are two phases in the REV: the sand and the pore.</p> <p>Figure S1 is uploaded with file name Figure S1-REV300.tif, which is the representative elemental volume (REV) used in this study</p>
Reliability of 3D image analysis and influence of contrast medium administration on measurement of Hounsfield unit values of the proximal femur
<p>These datasets consists of three PDF files for intraobserver ICC, interobserver ICC and effect of contrast medium administration.</p>
[LS2N_IPI_DIBR_Videos] 3D-TV System with Depth-Image-Based Rendering Architectures, Techniques and Challenges
<h3>Presentation & Goal</h3> <p>The <strong>IRCCyN IVC DIBR Videos</strong> quality database contains <strong>102 video sequences</strong> of 6 seconds in 1024x768 resolution with between 15 and 30 frames per second. Individual votes and Mean Opinion Scores (MOS) obtained by an Absolute Category Rating with Hidden Reference (ACR-HR) experiment are provided.</p> <p>Three different multiview plus depth (MVD) sequences are considered in this database. The sequences are Book Arrival (1024x768, 16 cameras with 6.5cm spacing), Lovebird1 (1024x768, 12 cameras with 3.5 cm spacing) and Newspaper (1024x768, 9 cameras with 5 cm spacing). Seven DIBR algorithms processed the three sequences to generate, for each sequence, four new viewpoints.</p> <h4>Initial goal</h4> <p>This database was done to consider the reliability of usual assessment methods when evaluating virtual synthesized views in the multiview video context. Virtual views are generated from Depth Image Based Rendering (DIBR) algorithms. Because DIBR algorithms involve geometric transformations, new types of artifacts come up. The question regards the ability of commonly used methods to deal with such artifacts.</p> <h4>Possible usage</h4> <p>This video quality database can be used to test a video quality metric precision in a context of DIBR.</p> <p> </p> <p> </p> <h3>Dataset description</h3> <div> <p>We used Absolute Category Rating - Hidden Reference (<strong>ACR-HR</strong>) and as test methodology.</p> <p>We provide a <a href="https://web.archive.org/web/20200219062815/ftp://ftp.ivc.polytech.univ-nantes.fr//IRCCyN_IVC_DIBR_Videos/IRCCyN_IVC_DIBR_Videos_Scores.xls">spreadsheet</a> with the individual scores, the Mean Opinion Score (MOS) and the Differential Mean Opinion Score (DMOS) for each of the 102 Processed Video Sequences (PVS).</p> <p>The <a href="https://web.archive.org/web/20200219062815/ftp://ftp.ivc.polytech.univ-nantes.fr//IRCCyN_IVC_DIBR_Videos/Videos">PVS (avi videos)</a> are available on our FTP server and are in the AVI format.</p> <p>The information about the source contents, video tested generation, subjective experiment environment and available data are provided in a <a href="https://web.archive.org/web/20200219062815/ftp://ftp.ivc.polytech.univ-nantes.fr//IRCCyN_IVC_DIBR_Videos/IRCCyN_IVC_DIBR_Videos_Description_v1.0.pdf">description file</a>.</p> <h4>Source contents (SRC)</h4> <table> <tbody> <tr> <th> <h4>Source number</h4> </th> <td> <h4>Source name</h4> </td> <td> </td> <td> <h4>Description</h4> </td> </tr> <tr> <th>01</th> <td><strong>Book Arrival</strong></td> <td> </td> <td><u>Description</u> : A man in an office standing up to welcome another man. <br><u>Author</u> : Heinrich-Hertz-Institut. <br><u>Video frame rate</u> : 15Hz <br><u>Number of frames</u> : 100</td> </tr> <tr> <th>02</th> <td><strong>Lovebird1</strong></td> <td> </td> <td><u>Description</u> : A couple walking in front of a temple. <br><u>Author</u> : Electronics and Telecommunications Research Institute (ETRI). <br><u>Video frame rate</u> : 30Hz <br><u>Number of frames</u> : 150</td> </tr> <tr> <th>03</th> <td><strong>Newspaper</strong></td> <td> </td> <td><u>Description</u> : A man and a girl reading the newspaper around a table when a man coming behind them. <br><u>Author</u> : Gwangju Institute of Science and Technology (GIST). <br><u>Video frame rate</u> : 30Hz <br><u>Number of frames</u> : 200</td> </tr> </tbody> </table> <p> </p> <p> </p> <h3>Hypothetical Reference Circuits (HRC)</h3> <div> <p>The Processed Video Sequences (PVS) were created using the following Hypothetical Reference Circuits (HRC).</p> <table> <tbody> <tr> <th>HRC name</th> <th>HRC description</th> </tr> </tbody> <tbody> <tr> <td>cam_xx</td> <td>[No impairment - Reference sequence view 1]</td> </tr> <tr> <td>cam_yy</td> <td>[No impairment - Reference sequence view 2]</td> </tr> <tr> <td>cam_zz</td> <td>[No impairment - Reference sequence view 3]</td> </tr> <tr> <td>A1</td> <td>Fehn cropped : where the depth map is pre-processed by a low-pass filter. Borders are cropped, and then an interpolation is processed to reach the original size.</td> </tr> <tr> <td>A2</td> <td>Fehn interpolated : Borders are inpainted by the method proposed by Telea</td> </tr> <tr> <td>A3</td> <td>Tanimoto et al., it is the recently adopted reference software for the experiments in the 3D Video group of MPEG.</td> </tr> <tr> <td>A4</td> <td>Müller et al., proposed a hole filling method aided by depth information.</td> </tr> <tr> <td>A5</td> <td>Ndjiki-Nya et al., the hole filling method is a patch-based texture synthesis.</td> </tr> <tr> <td>A6</td> <td>Köppel et al., uses depth temporal information to improve the synthesis in the disoccluded areas.</td> </tr> <tr> <td>A7</td> <td>corresponds to the unfilled sequences (i.e. with holes).</td> </tr> <tr> <td>cam_xx_qp26</td> <td>Reference sequence view 1 coded by H.264 with a QP = 26</td> </tr> <tr> <td>cam_xx_qp34</td> <td>Reference sequence view 1 coded by H.264 with a QP = 34</td> </tr> <tr> <td>cam_xx_qp44</td> <td>Reference sequence view 1 coded by H.264 with a QP = 44</td> </tr> </tbody> </table> <p>There are several references (3 first lines of the table) for each content, because there are the 3 views used for each content. So there are 3 HRC with no impairment.</p> <p> </p> <p> </p> <h3>Experiment information</h3> <div> <table> <tbody> <tr> <td> <h4>Features</h4> </td> <td> <h4>Value</h4> </td> </tr> <tr> <td><strong>Highlights of this database</strong></td> <td>Depth Images Based Rendering</td> </tr> <tr> <td>Free-viewpoint</td> </tr> <tr> <td>video quality</td> </tr> <tr> <td><strong>Video</strong></td> </tr> <tr> <td><strong>Frame frequency</strong></td> <td>30 or 15Hz</td> </tr> <tr> <td><strong>Video format</strong></td> <td>1024x768</td> </tr> <tr> <td><strong>Video length</strong></td> <td>100, 150 or 200 frames</td> </tr> <tr> <td><strong>Video duration</strong></td> <td>5 to 7 seconds</td> </tr> <tr> <td><strong>Number of videos</strong></td> <td>102</td> </tr> <tr> <td><strong>Subjective test</strong></td> </tr> <tr> <td><strong>Observation distance</strong></td> <td>4H</td> </tr> <tr> <td><strong>Environment</strong></td> <td>ITU-R BT.500-11</td> </tr> <tr> <td><strong>Background luminance</strong></td> <td>25cd/m²</td> </tr> <tr> <td><strong>Methodology</strong></td> <td>ACR</td> </tr> <tr> <td><strong>Duration</strong></td> <td>30 minutes</td> </tr> <tr> <td><strong>Pre screening</strong></td> <td>Snellen, Ishihara</td> </tr> <tr> <td><strong>Post screening (Rejection method)</strong></td> <td>VQEG Multimedia</td> </tr> <tr> <td><strong>Observers</strong></td> </tr> <tr> <td><strong>Number of observers</strong></td> <td>32 naives</td> </tr> <tr> <td><strong>Display</strong></td> </tr> <tr> <td><strong>Display</strong></td> <td>TVLogic LMV401</td> </tr> <tr> <td><strong>Display resolution</strong></td> <td>Full HD (1920x1080 pixels)</td> </tr> <tr> <td><strong>Display frequency</strong></td> <td>60Hz</td> </tr> <tr> <td><strong>Display mode</strong></td> <td>progressive</td> </tr> <tr> <td><strong>Display luminance max</strong></td> <td>180cd/m²</td> </tr> </tbody> </table> </div> </div> </div>
Multimodal 2D and 3D microscopic mapping of growth cartilage by computational imaging techniques – a short review including new research
<p>This dataset contains the phase and amplitude maps obtained with Fourier ptychographic microscopy and the X-ray diffraction tensor tomography dataset described in the journal article "Multimodal 2D and 3D microscopic mapping of growth cartilage by computational imaging techniques".</p>
CartoCell, a high-content pipeline for accurate 3D image analysis, unveils cell morphology patterns in epithelial cysts.
<p>CartoCell is a deep learning-based image processing pipeline for the high-content segmentation of whole epithelial cysts acquired at low resolution with minimal human intervention. This pipeline enables high-precision cell segmentation that allows a single cell cartography study of major interest for the search of cellular patterns.<br><br>Paper: https://www.cell.com/cell-reports-methods/pdf/S2667-2375(23)00249-7.pdf</p> <h2><strong>Cite this dataset</strong></h2> <h3><strong>Andrés-San Román, J. A., Gordillo-Vázquez, C., Franco-Barranco, D., Morato, L., Fernández-Espartero, C. H., Baonza, G., ... & Escudero, L. M. (2023). CartoCell, a high-content pipeline for 3D image analysis, unveils cell morphology patterns in epithelia. Cell Reports Methods, 3(10). https://doi.org/10.1016/j.crmeth.2023.100597</strong></h3> <p><strong> </strong></p> <div>More info available at: https://data.mendeley.com/datasets/7gbkxgngpm</div>
Figure 3d from: Egloff W, Agosti D, Kishor P, Patterson D, Miller J (2017) Copyright and the Use of Images as Biodiversity Data. Research Ideas and Outcomes 3: e12502. https://doi.org/10.3897/rio.3.e12502
Figure 3d - From Waterman (1978).
Figure 5 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
Figure 5 Workflow of image masking using front- and back-light information.
ScienceDex guides
Understand access before you commit
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.