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60 results for “Depth Image”
Figure 8 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 8 Comparison of images taken with a Keyence VHX 5000 digital microscope (lens: Z20, A–C) and DISC3D (D). The whole specimen of Pogonocherus hispidus can be imaged at once with the VHX 5000 with a X30-magnification (A). To compare the digital resolution, we focus on the pronotum of the beetle (B: VHX 5000, ×30; C: VHX 5000, ×100, D: DISC3D, ×1.26). Scale bars: 1 mm.
Figure 7 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 7 Osmia adunca, two exemplary raw images of the front-light stack, with the focal plane going through the proximal (A) and the distal part (B) of the sample, the EDOF image (C), and a detail of the latter (D) demonstrate the resolution. Scale bars: 1 mm.
Figure 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
Figure 2 Illumination by two hemispherical white-coated domes (A–C). The back-light-dome can be removed for specimen mounting (D, E). No direct light from the LED-stripes hits the specimens (C, E).
Figure 17 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 17 Relation of surface area and volume for all insect species presented here. While the dipteran, hymenopteran and lepidopteran species had their wings unfolded, all beetles had the wings folded underneath their elytra. Models are not to scale.
Figure 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
Figure 3 The camera is mounted on a macro-rail (A). The camera position and orientation can be fine-tuned in all directions (B–D). The camera lens is covered by a pinhole-cap (B).
Figure 10 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 10 Comparison of dense-cloud-based mesh generation and visual consistency meshing of Thricops sp. (A EDOF image). Thin and delicate structure like wings and setae are not well modelled from the dense cloud (B) but well preserved by visual consistency meshing (C).
Figure 11 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 11 Comparison of mesh quality and number of polygons, exemplified by a model of the shell of Discus rotundatus. The model with 1 million faces (A) has a file size (3D-PDF) of 35 MB and shows more detail, but the reduced model with 75.000 faces (B) still well resembles the structure with a file size (3D-PDF) of only 3 MB.
Figure 9 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 9 Workflow of model generation of Pogonocherus hispidus with PhotoScan Pro. In total, 398 EDOF-images are taken with DISC3D (one example is shown in A). Using the image data, masks and camera positions estimated with the calibration sphere (see Fig. 6), a sparse cloud with optimized camera positions is generated (B). Two options for model generation are available: direct mesh calculation based on a dense point cloud (C) or meshing with visual consistency (D). Resulting meshes can be textured (E, F). Scale bar: 1mm.
Figure 12 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 12 Overview and size comparison of the specimens used in this study. Coleoptera: a Prosopocoilus savagei b Anoplotrupes stercorosus, *: specimen was broken during comparative measurements c Stenocorus meridianus d Typhaeus typhoeus e Rutpela maculata f Valgus hemipterus g Cryptocephalus sericeus h Pogonocherus hispidus i Phyllobius pyri j Tytthaspis sedecimpunctata; Lepidoptera: k Zygaena filipendulae; Hymenoptera: l Paraponera clavata m Osmia adunca n Sphecodes ephippius; Diptera: o Thricops sp., p Culex pipiens q Oscinella frit; Gastropoda: r Helicodonta obvoluta s Aegopinella nitens t Discus rotundatus.; Scale bar: 1 cm (keep in mind that not all specimens are equidistant to the lens; i.e., at the same height of the needle).
Labeled RGB and depth images for cattle body condition score prediction
<p>This dataset contains a collection of preclassified Criollo cow RGB+depth videos, as well as processed depth, grayscale, and edge images. The dataset was previously used to train convolutional neural networks and vision transformers to estimate body condition scores of cattle, and will be useful to other researchers in need a high quaility visual dataset that incorporates depth for the purposes of three dimensional representations of cattle. The availability of compatible software and image processing packages makes this dataset very robust and applicable to other areas of both agricultural and machine learning research. This dataset has several features that make it a good test of machine learning algorithms such as low sample uniqueness and a slightly subjective metric.</p>
Data from: Depth image super-resolution reconstruction based on a modified joint trilateral filter
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Data from: 3D sorghum reconstructions from depth images identify QTL regulating shoot architecture
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Data from: Retrospective study using magnetic resonance imaging (MRI) to measure depths of acupuncture points in neck and shoulder region
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Labeled RGB and depth images for cattle body condition score prediction
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GHRSST Level 2P 1 m Depth Global Sea Surface Temperature version 3.0 from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi NPP satellite (GDS2)
A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS). This sensor resides on the Suomi National Polar-orbiting Partnership (Suomi_NPP) satellite launched on 28 October 2011. VIIRS is a whiskbroom scanning radiometer which takes measurements in the cross-track direction within a field of regard of 112.56 degrees using 16 detectors and a double-sided mirror assembly. At a nominal altitude of 829 km, the swath width is 3060 km, providing full daily coverage both on the day and night side of the Earth.The VIIRS instrument is a 22-band, multi-spectral scanning radiometer that builds on the heritage of the MODIS , AVHRR and SeaWIFS sensors for sea surface temperature (SST) and ocean color. For the infrared bands for SST the effective pixel size is 750 meters at nadir and the pixel size variation across the swath is constrained to no more than 1600 meters at the edge of the swath. This L2P SST v3.0 is upgraded from the v2.0 with several significant improvements in processing algorithms, including contamination detection, cloud detection, and data format upgrades. It contains the global near daily-coverage Sea Surface Temperature at 1-meter depth with 750 m (along) x 750 m (cross) spatial resolution in swath coordinates. Each netCDF file has 768 x 3200 pixels in size, in compliance with the GHRSST Data Processing Specification (GDS) version 2 format specifications.
[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>
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.
Figure 4 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 4 Workflow of EDOF-calculation. For a detailed description, see Suppl. material 1: S2.
Figure 18 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 18 Interactive, textured 3D-model of Anoplotrupes stercorosus (21 mm body size).
Figure 16 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 16 Interactive 3D-model of Helicodonta obvoluta (9 mm shell diameter).
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.