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Dataset results
422 results for “textures”
[CCO] Decal - Graffiti Textures
**Totaly Free** [CC0 1.0 Universal (CC0 1.0) Public Domain Dedication ](https://creativecommons.org/publicdomain/zero/1.0/deed.en) **Includes Pack:** - Albedo (PNG file) - Opaticy (PNG file) **Textures size- 4096x4096** Source: Objaverse 1.0 / Sketchfab
odm_textured_model_geo
Fotogrametria, con el programa Open Drone Map. Fotos descargadas en CyArk Source: Objaverse 1.0 / Sketchfab
Seashell (Pixelio + Memento) no texture
Seashell scanned with Pixelio & Autodesk Memento Pixelio on Kickstarter: https://www.kickstarter.com/projects/1886998349/pixelio-3d-multiscanner Source: Objaverse 1.0 / Sketchfab
Arc de Triomphe, Paris (with texture)
Built from about 250 photos of 10MPixel with Smart3DCapture. The top is not built because all photos are taken from the ground. Source: Objaverse 1.0 / Sketchfab
Collection of textures in colorectal cancer histology
<p><strong>Content</strong></p> <p>This data set represents a collection of textures in histological images of human colorectal cancer. It contains two files:</p> <ol> <li>"Kather_texture_2016_image_tiles_5000.zip": a zipped folder containing 5000 histological images of 150 * 150 px each (74 * 74 µm). Each image belongs to exactly one of eight tissue categories (specified by the folder name). </li> <li>"Kather_texture_2016_larger_images_10.zip": a zipped folder containing 10 larger histological images of 5000 x 5000 px each. These images contain more than one tissue type. </li> </ol> <p><strong>Image format</strong></p> <p>All images are RGB, 0.495 µm per pixel, digitized with an Aperio ScanScope (Aperio/Leica biosystems), magnification 20x. Histological samples are fully anonymized images of formalin-fixed paraffin-embedded human colorectal adenocarcinomas (primary tumors) from our pathology archive (Institute of Pathology, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany).</p> <p><strong>Ethics statement</strong></p> <p>All experiments were approved by the institutional ethics board (medical ethics board II, University Medical Center Mannheim, Heidelberg University, Germany; approval 2015-868R-MA). The institutional ethics board waived the need for informed consent for this retrospective analysis of anonymized samples. All experiments were carried out in accordance with the approved guidelines and with the Declaration of Helsinki.</p> <p><strong>More information / data usage</strong></p> <p>For more information, please refer to the following article. <strong>Please cite this article when using the data set.</strong></p> <p>Kather JN, Weis CA, Bianconi F, Melchers SM, Schad LR, Gaiser T, Marx A, Zollner F: Multi-class texture analysis in colorectal cancer histology (2016), Scientific Reports (in press)</p> <p><strong>Contact</strong></p> <p>For questions, please contact:<br /> Dr. Jakob Nikolas Kather<br /> http://orcid.org/0000-0002-3730-5348<br /> ResearcherID: D-4279-2015</p>
MM-S2TXTR-F03 Textural features of mammographic masses, Greece, Feb.2003
<p>========================================================</p> <p> Dataset: MM-S2TXTR-F03</p> <p> Textural features of mammographic masses<br /> Greece, Feb.2003<br /> <br /> Release Notes</p> <p> Copyright (c) 2016 by Harris V. Georgiou</p> <p>========================================================<br /> Release: Aug 3, 2016</p> <p> - Version: 1.1a<br /> - Format: .mat<br /> ========================================================</p> <p><br /> This file contains important information about the current version of the dataset package. Downloading and using this material hints that you accept the EULA/Terms-of-Use (please read carefully).</p> <p>We welcome your comments and suggestions.</p> <p>_______________________________________________<br /> WHAT'S IN THIS PACKAGE?</p> <p>- Overview<br /> - Available file formats<br /> - Files and Datasets<br /> - License Agreement</p> <p>_______________________________________________<br /> OVERVIEW</p> <p>Localized texture analysis of breast tissue on mammograms is an issue of major importance in mass characterization. This material aims to the establishment of a quantitative approach of mammographic masses texture classification based on these datasets of the extracted textural features.</p> <p>This package contains an extensive set of textural feature datasets, in multiple configurations and scales, constructing compact sets of textural "signatures" for benign and malignant cases of tumors in mammograms. The datasets refer to 142 localized masses in mammograms, as well as a set of qualitative features that have been proven as of utmost importance regarding the true pathology in each case. For the construction of the raw material for these datasets, 130 of the 142 MLO mammograms containing a mass were selected by an expert physician for digitization, including a total of 46 benign mass cases and 84 mass malignancies of various types (12 were marked 'unreliable').</p> <p>The files also include the full texture data of 7,000-13,000 sampling "boxes" of sizes 20 and 50 pixels, as well as complete-mass and borderline-only sampling areas.</p> <p>The digitized mammograms were subsequently transformed digitally to a resolution of 63 mm (400 dpi) at 8-bit gray level, which is consistent with other typical image databases of digitized mammograms that are used as a reference in similar studies.</p> <p>Detailed information about the files and the structure of the datasets are available in the manifest file (S2_data_texture_directory.pdf).</p> <p>_______________________________________________</p> <p>AVAILABLE FILE FORMATS</p> <p>The datasets are available in the following formats (included):</p> <p>*.mat : Matlab/Octave native data file (workspace)</p> <p> </p>
Low-Amplitude Textures Explored with the Bare Finger: Roughness Judgments Follow an Inverted U-Shaped Function of Texture Period Modified by Texture Type
<p>Roughness is probably the most salient dimension pertaining to the perception of textures by touch and has been widely investigated. There is a controversy on how roughness relates to the texture’s spatial period and which factors influence this relation. Here, roughness during bare finger exploration of coarse textures is studied for different types of textures with elements of low height (0.3 mm). Participants were presented with square-wave gratings that were defined along one dimension and sine-wave gratings that were defined along one or two dimensions. Textures of each type varied in their spatial half period between 0.25 and 5.17 mm. Participants explored the textures by a lateral movement or a stationary finger contact. In all conditions judged roughness increased with spatial period up to a peak roughness and then decreased again. The exact function depended on the texture type, but hardly on exploration mode. We conclude that roughness is an inverted U-shaped function of texture period, if the textures are of low amplitude. The effects are explained by the interplay of two components contributing to the spatial code to roughness: variability in skin deformation due to the finger’s intrusion into the texture, which increases with the textures’ period up to a maximum (when the skin contacts the texture’s ground), and variability associated with the spatial frequency of the deformation, which decreases with spatial period.</p> <p><strong>Drewing</strong>, K. (2016). Low-Amplitude Textures Explored with the Bare Finger: Roughness Judgments Follow an Inverted U-Shaped Function of Texture Period Modified by Texture Type. <em>Haptics: Perception, Devices, Control, and Applications </em>(pp. 206-217). Springer: Heidelberg.</p> <p> </p> <p>The file DataPerTrialAndVp_Zenodo.txt contains all data relative to the publication.</p> <p>A description of the variables is contained in the file VARIABLE_CODES.txt</p>
Polygon Laser Texturing Process
<p>Polygon Laser Texturing Process</p>
Laser Texturing Process
<p>Laser Texturing Process</p>
Prompted Textures Dataset (PTD)
<p>The Prompted Textures Dataset (PTD) is a synthetic texture image dataset consisting of 246,285 images across 56 different texture classes from the work <a title="On Synthetic Texture Datasets: Challenges, Creation, and Curation" href="https://www.arxiv.org/abs/2409.10297" target="_blank" rel="noopener">On Synthetic Texture Datasets: Challenges, Creation, and Curation</a>.</p> <p>If you find this dataset useful in your work, please consider citing:</p> <p>@misc{hoak2024synthetictexturedatasetschallenges,<br> title={On Synthetic Texture Datasets: Challenges, Creation, and Curation}, <br> author={Blaine Hoak and Patrick McDaniel},<br> year={2024},<br> eprint={2409.10297},<br> archivePrefix={arXiv},<br> primaryClass={cs.CV},<br> url={https://arxiv.org/abs/2409.10297}, <br>}</p> <p> </p>
Ullevi_GasDilln2005_8_target_textur
Ullevi, Gåsinge-Dillnäs 207:8, Södermanland. Hällristning med lång skålgropsrad, skeppsfigurer mm. Sörmlands museum och Opus-Heritas. 3D_SFM Source: Objaverse 1.0 / Sketchfab
Mount Russell ogham stone (textured)
Discovered in a field near the house called Mount Russell, near Kilfinnane, Co. Limerick and possibly from a nearby early church site. Moved by the 1940s to the Glen of Aherlow (at Aherlow House Hotel), Co. Tipperary where it still remains. Source: Objaverse 1.0 / Sketchfab
Bred 136 Boda Droner Textur
Bred 136, Boda, Uppland. Drönare. Hällristning. Opus Heritas Fotogrammetri 3D-SfM av Catarina Bertilsson. Stockholms universitet, "Digitala bilder för forskning och publik". Source: Objaverse 1.0 / Sketchfab
Cannon Environment Textured
My Textured Environment with Textured Cannon model for my W10: Create Textures Assignment for DIG4324C class. Source: Objaverse 1.0 / Sketchfab
Ullevi Gas Dilln 207 Omr1 9 17 18 Textur
Ullevi, Gåsinge-Dillnäs 207 område 1-4, 6-9, 17, 18. Södermanland. Hällristning med skeppsfigurer, skålgropar mm. Sörmlands museum och Opus-Heritas. 3D_SFM. Source: Objaverse 1.0 / Sketchfab
Benic Reformed Church_no_texture
A low quality model (here decimated to 2000k, no texture) of the ruins of the Reformed Church at Benic, Alba county, Romania. This is a test, no texture, in order to display the inner structure of the tower, processed within the model. More will follow soon. (copyright Universitatea 1 Decembrie 1918 din Alba Iulia, 2017). The model is based on 662 photos, taken with 28 mm lens from ground level. Source: Objaverse 1.0 / Sketchfab
[CCO] Decal - Graffiti Textures
**Totaly Free** [CC0 1.0 Universal (CC0 1.0) Public Domain Dedication ](https://creativecommons.org/publicdomain/zero/1.0/deed.en) **Includes Pack:** - Albedo - Opaticy **Textures size- 2048x4096** Source: Objaverse 1.0 / Sketchfab
Island ogham stone (textured)
https://ogham.celt.dias.ie/stone.php?lang=en&site=Island&stone=300._Island&stoneinfo=description Source: Objaverse 1.0 / Sketchfab
Nämforsen Notön G2 Ådals-Liden 193 Ång. Textur
Nämforsen, Notön, Hallström G2, Ådals-Liden 193, Ångermanland. For more information see www.namforsen.com and www.shfa.se Source: Objaverse 1.0 / Sketchfab
Kuelap Ruins - Watertight textured mesh
Kuelap Ruins [[ this textured model was created using only the pointcloud, no original images were used in the generation of the final textures ]] made from only 55 captured frames from the following video were observed and measured using colmap dense stereo photogrammetry: https://www.youtube.com/watch?v=L1rte2FnK04 We thank Trans-Americas Journey for their wonderful travel and cultural heritage videos!! more information about this location can be found here: https://en.wikipedia.org/wiki/Ku%C3%A9lap Tools used: youtube-dl, ffmpeg, python, colmap, houdini https://www.instagram.com/organiccomputer/ Source: Objaverse 1.0 / Sketchfab
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.