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63 results for “Multi view”
Presents: a static 5x5 RGB multi-view dataset
<h1><strong>Description</strong></h1> <p>This is an RGB static multi-view test sequence captured by a Microsoft Kinect Azure DK. The captures of the sequence have been captured using a xyz actuator that allows to position the camera in a volume of 0.82x1.2x0.85 meters. It comprises 25 views arranged in a 5x5 array with a separation of 1 cm in x and y axes. The resolution of the images are 1920x1080.</p> <h2><strong>Institutions</strong></h2> <p>Research Center on Software Technologies and Multimedia Systems for Sustainability (CITSEM), Universidad Politécnica de Madrid (UPM), Madrid, Spain.</p> <p>Laboratory of Image Synthesis and Analysis, LISA department, Ecole Polytechnique de Bruxelles, Universite Libre de Bruxelles, Belgium.</p> <h2><strong>Folder structure</strong></h2> <p>The dataset is composed of:</p> <ul> <li>The calibration file: cam_params.json</li> <li>The captured views in .yuv files: 1920x1080 8bits YUV420p</li> </ul> <h1><strong>Terms of use</strong></h1> <p>Any kind of publication or report using this dataset should refer to this DOI.</p>
Deep Clustering Representation for Spatially Resolved Transcriptomics Data via Multi-view Variational Graph Auto-Encoders with Consensus Clustering
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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.
MvGraphDTA: Multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs
<h1>MvGraphDTA</h1> <p>MvGraphDTA:通过引入图形和折线图,基于多视图的图形深度模型用于药物-靶点亲和力预测</p> <h2>要求</h2> <p><span><span>numpy</span></span><span>==1.23.5</span></p> <p><span><span>pandas</span></span><span>==1.5.2</span></p> <p><span><span>biopython</span></span><span>==1.79</span></p> <p><span><span>scipy</span></span><span>==1.9.3</span></p> <p><span><span>torch</span></span><span>==2.0.1</span></p> <p><span><span>torch_geometric</span></span><span>==2.3.1</span></p> <h2>示例用法</h2> <h3>1. 使用我们的预训练模型</h3> <p>在本节中,我们提供了 pdbbindv2016 的核心集数据和 Li 的数据(过滤后的 casf2013 和 casf2016),您可以直接执行以下命令来运行我们的预训练模型并在核心集上获取结果。</p> <pre># Run the following command.<br>python test_pretrain.py</pre> <h3>2. 在数据集上运行</h3> <p>在本节中,您必须提供药物的 .sdf 文件以及靶标的 .pdb 文件。</p> <div> <p># 您可以通过运行以下命令获取药物和靶点的图形和折线图。<br>Python data_process.py</p> <p># 当所有数据都准备好后,您可以通过运行以下命令来训练自己的模型。<br>Python training.py</p> </div>
Flame Tomography in a Mirror-Based Multi-View Configuration
<p>The data is the image-based tomographic reconstruction of the flame. The multiview images are obtained from the mirror and a single high-speed camera (High-Speed Vision Research Phantom UHS v1612).</p>
Data from: A multi-tissue view on telomere dynamics and postnatal growth
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Spatial-MGCN: a novel multi-view graph convolutional network for identifying spatial domains with attention mechanism
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Dataset for "Decomposing God Header File via Multi-View Graph Clustering"
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Text-fig. 5. Anacardiaceae (a–m), Burseraceae (n–q). Scale bars = 1 cm. a–f: Pentoperculum sp. a–c: USNM PAL 772360. a: Lateral view of endocarp, apex up; three germination valves visible, the central clearly displaying the bipartite nature of the valve, reflected light, palladium coated. b: Apical view displaying six locules, with two preserved germination valves at the lower left, reflected light, palladium coated. c: Basal view of the endocarp, the locules suggested by swellings; note point of attachment, micro-CT scan surface rendering. d–f: Pentoperculum sp. USNM PAL 772359, reflected light, palladium coated. d: Lateral view of a probable 6-loculed endocarp, apex up; a single intact germination valve in the center, displaying the central lineation that divides it in two. e: Apical view; two bi-partite germination valves are visible, indicated by arrows to the middle cleavage line of two of the valves. f: Basal view, the locules suggested by the undulations in the margin. g–i: Indet. Spondioideae. USNM PAL 772358, reflected light, palladium coated. g: Lateral view of multi-locular endocarp, apex up. h: Apical view showing finely punctuate surface and peripheral locule cavities. i: Basal view. j–m: Cf. Pleiogynium USNM PAL 772357. j: Lateral view of the multi-locular endocarp, apex up; note intact germination valve on left and exposed locule facing the viewer, micro-CT scan surface rendering. k: Lateral view, rotated about 30° from (j), showing three exposed locules, reflected light, palladium coated; note bipartite locule lining at center. l: Apical view of the multilocular endocarp; the locule with intact germination valve at the upper right, reflected light, palladium coated. Arrows to each locule. m: Basal view showing central point of attachment and prominent radiating ridges aligned with the locules, micro-CT scan surface rendering. n–q: Canarium, USNM PAL 772361. Scale bar = 1 cm. n: Lateral view of endocarp directly facing one germination valve flanked by two strong ridges; apex up; specimen coated in sodium nitrate and photographed by R. A. Scott. o: Lateral view facing one of the three pronounced ridges, flanked to the left and right by two germination valves; apex up. p: Apical view displaying the three strong ridges, arching over the apex and flanking three deep embayments, covered with germination valves. q: Basal view, the three ridges being less pronounced. o–q: Reflected light, palladium coated. in The Early Middle Eocene Wagon Bed Carpoflora Of Central Wyoming, U.S.A.
Text-fig. 5. Anacardiaceae (a–m), Burseraceae (n–q). Scale bars = 1 cm. a–f: Pentoperculum sp. a–c: USNM PAL 772360. a: Lateral view of endocarp, apex up; three germination valves visible, the central clearly displaying the bipartite nature of the valve, reflected light, palladium coated. b: Apical view displaying six locules, with two preserved germination valves at the lower left, reflected light, palladium coated. c: Basal view of the endocarp, the locules suggested by swellings; note point of attachment, micro-CT scan surface rendering. d–f: Pentoperculum sp. USNM PAL 772359, reflected light, palladium coated. d: Lateral view of a probable 6-loculed endocarp, apex up; a single intact germination valve in the center, displaying the central lineation that divides it in two. e: Apical view; two bi-partite germination valves are visible, indicated by arrows to the middle cleavage line of two of the valves. f: Basal view, the locules suggested by the undulations in the margin. g–i: Indet. Spondioideae. USNM PAL 772358, reflected light, palladium coated. g: Lateral view of multi-locular endocarp, apex up. h: Apical view showing finely punctuate surface and peripheral locule cavities. i: Basal view. j–m: Cf. Pleiogynium USNM PAL 772357. j: Lateral view of the multi-locular endocarp, apex up; note intact germination valve on left and exposed locule facing the viewer, micro-CT scan surface rendering. k: Lateral view, rotated about 30° from (j), showing three exposed locules, reflected light, palladium coated; note bipartite locule lining at center. l: Apical view of the multilocular endocarp; the locule with intact germination valve at the upper right, reflected light, palladium coated. Arrows to each locule. m: Basal view showing central point of attachment and prominent radiating ridges aligned with the locules, micro-CT scan surface rendering. n–q: Canarium, USNM PAL 772361. Scale bar = 1 cm. n: Lateral view of endocarp directly facing one germination valve flanked by two strong ridges; apex up; specimen coated in sodium nitrate and photographed by R. A. Scott. o: Lateral view facing one of the three pronounced ridges, flanked to the left and right by two germination valves; apex up. p: Apical view displaying the three strong ridges, arching over the apex and flanking three deep embayments, covered with germination valves. q: Basal view, the three ridges being less pronounced. o–q: Reflected light, palladium coated.
Millimeter-wave Multi-View Radar (MMVR) Dataset
<p><strong>Introduction</strong></p> <p>The Millimeter-wave Multi-View Radar (MMVR) dataset comprises 345K multi-view radar frames collected from 25 human subjects over 6 different rooms. It includes 446K annotated bounding boxes/segmentation instances, and 7.59 million annotated keypoints to support three perception tasks: object detection, pose estimation, and instance segmentation in the image plane.</p> <p>MMVR consists of 35 data sessions, each stored in a data folder with the format d<em>x</em>sy, where <em>x</em> represents the day and y the session index of that day when the data were collected.</p> <p>Within each data folder, data frames are grouped into 395 non-overlapping one-minute data segments. Each data segment is stored in a folder named using a three-digit, zero-filled convention based on the chronological order. For example, the first one-minute data segment is saved in the folder 000, while the second one-minute data segment is stored in the folder 001.</p> <p>Within each data segment folder, each data frame consists of 5 NPZ files: meta, radar, bounding boxes (bbox), keypoints (pose) and segmentation masks (mask). Each data frame is named using a five-digit, zero-filled convention based on the chronological order within the one-minute data segment.</p> <pre><code>Root/ ├── d1s1/ │ └── ... ├── d1s2/ │ ├── 000/ │ │ ├── 00000_meta.npz ... Meta info │ │ ├── 00000_radar.npz ... Horizontal/Vertical heatmaps │ │ ├── 00000_bbox.npz ... 2D Bounding Boxes │ │ ├── 00000_pose.npz ... 2D keypoints │ │ ├── 00000_mask.npz ... 2D Segmentation masks │ │ ├── 00001_meta.npz │ │ ├── 00001_radar.npz │ │ ├── 00001_bbox.npz │ │ ├── 00001_pose.npz │ │ ├── 00001_mask.npz │ │ . │ │ ├── 00899_meta.npz │ │ ├── 00899_radar.npz │ │ ├── 00899_bbox.npz │ │ ├── 00899_pose.npz │ │ └── 00899_mask.npz │ └── 001/ │ ├── 00000_meta.npz │ ├── 00000_radar.npz │ ├── 00000_bbox.npz │ ├── 00000_pose.npz │ ├── 00000_mask.npz . └── ... ├── d9s5/ │ └── ... └── d9s6/ └── ... </code></pre> <p> </p> <p><strong>At a Glance</strong></p> <ul> <li> <ul> <li>The size of the unzipped dataset is ~81.8 GB</li> <li>The MMVR dataset consists of 345K data frames with each frame including 5 NPZ files: meta, radar, bounding boxes (bbox), keypoints (pose) and segmentation masks (mask).</li> <li>The MMVR dataset is divided into four smaller chunks to facilitate easier downloads: three chunks of approximately 20 GB each and one chunk of 12 GB. <ul> <li>P1.zip (24.2 GB): <ul> <li>README.md and its related figure folder (figs)</li> <li>load_sample.ipynb: a python snippet to load and visualize a data frame</li> <li>data_split.npz: an npz file containing the predefined data splits (S1 or S2) for training, validation, and test sets of data segments under P1 and P2.</li> <li>all data frames under P1 (d1s1, d1s2, d2s2, d3s1, d3s2, d4s1)</li> </ul> </li> <li>P2_00.zip (20.4 GB): all data frames in d5s1 – d5s6 and d6s1 – d6s6</li> <li>P2_01.zip (21.6 GB): all data frames in d7s1 – d7s5 and d8s1 – d8s6</li> <li>P2_02.zip (11.7 GB): all data frames in d9s1 – d9s6</li> </ul> </li> </ul> </li> </ul> <p><strong>Citation</strong></p> <p>If you use the MMVR dataset in your research, please cite our contribution:</p> <pre><code>@inproceedings{MMVR2024, title={MMVR: Millimeter-wave Multi-View Radar Dataset and Benchmark for Indoor Perception}, author={M. Mahbubur Rahman and Ryoma Yataka and Sorachi Kato and Pu Perry Wang and Peizhao Li and Adriano Cardace and Petros Boufounos}, booktitle={Proceedings of European Conference on Computer Vision (ECCV)}, pages={}, year={2024} }</code></pre> <p><strong>License</strong></p> <p>The MMVR dataset is released under <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA-4.0 license</a>.</p> <p>All data:</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2023 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre>
raycast: multi-view object detection in UAV image clouds (case-study data)
<p>This package contains image data for conducting multi- and single-view sewer inlet detection in UAV image clouds. The package consists in: - individual UAV images, taken with high overlap and corrected for lens distortion - orthophoto of the case study area, clipped to road boundaries.</p>
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.
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.