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422 results for “textures”
MatSim Dataset and benchmark for one-shot visual materials and textures recognition
<p><strong>The MatSim Dataset and benchmark</strong></p> <p>Synthetic dataset and real images benchmark for visual similarity recognition of materials and textures.</p> <p>MatSim: a synthetic dataset, a benchmark, and a method for computer vision-based recognition of similarities and transitions between materials and textures focusing on identifying any material under any conditions using one or a few examples (one-shot learning).</p> <p>Based on the paper: <a href="https://arxiv.org/pdf/2212.00648.pdf">One-shot recognition of any material anywhere using contrastive learning with physics-based rendering</a></p> <p> </p> <p><strong>Benchmark_MATSIM.zip: </strong>contain the benchmark made of real-world images as described in the paper</p> <p><strong>Dataset Generation Scripts.zip: </strong>Contain the Blender (4.1) Python scripts used for generating the dataset<br><br><a href="https://zenodo.org/record/7390166/files/MatSim_object_train_split_1.zip?download=1"><strong>MatSim_object_train_split_1,2,3....zip:</strong> </a>Contain a subset of the synthetics dataset for images of CGI images materials on random objects as described in the paper.</p> <p><strong>MatSimTrainObjectsNearField_.zip </strong>Contain train sets with near fieldlight sources</p> <p><strong><a href="https://zenodo.org/record/7390166/files/MatSim_Vessels_Train_1.zip?download=1">MatSim_Vessels_Train_1,2,3....zip </a></strong><a href="https://zenodo.org/api/files/020f90b2-7c41-44ad-86e3-69257884a569/MatSim_object_train_split_1.zip"><strong>:</strong> </a>Contain a subset of the synthetics dataset for images of CGI images materials inside transparent containers as described in the paper.<br><br><strong>*Note: these are subsets of the dataset; the full dataset can be found at:</strong><br><a href="https://e1.pcloud.link/publink/show?code=kZIiSQZCYU5M4HOvnQykql9jxF4h0KiC5MX">https://e1.pcloud.link/publink/show?code=kZIiSQZCYU5M4HOvnQykql9jxF4h0KiC5MX</a></p> <p>or<br><a href="https://icedrive.net/s/A13FWzZ8V2aP9T4ufGQ1N3fBZxDF">https://icedrive.net/s/A13FWzZ8V2aP9T4ufGQ1N3fBZxDF</a></p> <p> </p>
Transition between distinct hybrid skyrmion textures through their hexagonal-to-square crystal transformation in a polar magnet
<p>The file Manuscript data files.7z contains the experimental data used for creating the figures in the manuscript entitled "Transition between distinct hybrid skyrmion textures through their hexagonal-to-square crystal transformation in a polar magnet" that appear in Nature Communications 14, 8050 (2023).</p><p>Paper abstract: Magnetic skyrmions, topological vortex-like spin textures, garner significant interest due to their unique properties and potential applications in nanotechnology. While they typically form a hexagonal crystal with distinct internal magnetisation textures known as Bloch- or Néel-type, recent theories suggest the possibility for direct transitions between skyrmion crystals of different lattice structures and internal textures. To date however, experimental evidence for these potentially useful phenomena have remained scarce. Here, we discover the polar tetragonal magnet EuNiGe3 to host two hybrid skyrmion phases, each with distinct internal textures characterised by anisotropic combinations of Bloch- and Néel-type windings. Variation of the magnetic field drives a direct transition between the two phases, with the modification of the hybrid texture concomitant with a hexagonal-to-square skyrmion crystal transformation. We explain these observations with a theory that includes the key ingredients of momentum-resolved Ruderman–Kittel–Kasuya–Yosida and Dzyaloshinskii-Moriya interactions that compete at the observed low symmetry magnetic skyrmion crystal wavevectors. Our findings underscore the potential of polar magnets with rich interaction schemes as promising for discovering new topological magnetic phases.</p>
Neural Texture Puppeteer (NeTePu; Dataset)
<p>This data entry contains the <strong>NePuMoo dataset </strong>from<strong> </strong>the<strong> <a href="https://arxiv.org/abs/2311.17109" target="_blank" rel="noopener">Neural Texture Puppeteer WACV Workshop "CV4Smalls" 2024 paper</a></strong>.</p> <p>This data entry contains a public release of the synthetic NePuMoo dataset from the Neural Texture Puppeteer paper. The data contains 12 synthetic cows with different texture. We provide NNOPCS maps, depth maps, RGB images, silhouettes and 3D keypoints. We also provide the camera parameters of the 24 camera views. The data was rendered in Blender by Ole Johannsen.</p>
Dataset from 'Topological interfaces crossed by defects and textures of continuous and discrete point group symmetries in spin-2 Bose-Einstein condensates'
<p>Dataset associated with the publication 'Topological interfaces crossed by defects and textures of continuous and discrete<br>point group symmetries in spin-2 Bose-Einstein condensates' in Physical Review Research. <br>Source data for Figures 3-8 in the manuscript.</p>
Human Psychophysics Dataset on Figure Ground Segregation in Texture Stimuli
<div> <div>The dataset is derived from a psychophysics experiment where 8 participants discriminated the orientation of a rectangular figure within a texture stimulus comprised of Gabor annuli. The figure region differed from the background in its contrast distribution, controlled by two independent variables: Contrast Heterogeneity and Grid Coarseness. Contrast Heterogeneity refers to the range of contrasts exhibited by Gabor annuli. In this experiment, there were five values for Contrast Heterogeneity in the figure: 0.01, 0.2575, 0.505, 0.7525, and 1. The contrast distribution of the background was always maximally heterogeneous with Contrast Heterogeneity equal to 1. Grid Coarseness, on the other hand, refers to the scaling factor that controls the spacing between the Gabor annuli. It determines the density of the grid pattern in the background <em>and</em> the figure region. In this experiment, there were five values for Grid Coarseness: 1, 1.125, 1.250, 1.375, and 1.5. The experiment consisted of 9 sessions, each containing multiple blocks of trials. Each block of trials contained 25 unique stimulus conditions, defined by the combination of Contrast Heterogeneity and Grid Coarseness.</div> <div> <p><strong>Stimuli, Tasks, and Procedure </strong></p> </div> <div> <p>Each texture stimulus consisted of a full-screen irregular grid of non-overlapping Gabor annuli placed on a grey background. The Gabor annuli had a diameter of 0.7°, a spatial frequency of 5.7 cycles/degree, and a mean luminance of 60.76 Cd/m2. Embedded within this texture was a rectangular figure region located in the lower right quadrant of the screen, which differed from the rest of the texture in the contrast distribution of its annuli. For sessions 1-8, the figure center was placed at an eccentricity of (7 ± 1)° but slightly varied in terms of polar angle such that it was completely inside the lower right quadrant. For session 9 (transfer session), the figure was placed in the upper left quadrant. </p> </div> <div> <p>Participants were required to indicate whether the rectangular figure was oriented horizontally or vertically by pressing the right and left arrow keys, respectively. The experiment employed a two-alternative forced-choice design, in which participants had to make a decision about the orientation of the figure in each trial. Responses were given with the middle and index fingers of the right hand. In each trial, the stimulus was presented for 1000 ms or less if the participant lost fixation or provided a response. Participants were required to maintain fixation throughout the presentation of the stimulus. </p> </div> <div> <p>After each trial, participants received feedback on their response. If the response was correct, a green fixation point was presented for 500 ms. If the response was incorrect, a red fixation point was presented for 500 ms. </p> </div> <div> <p>The experiment was conducted in a dimly lit room. A chin and head-rest was used to support the participant's head and to keep eye-screen distance constant at 57 cm. Stimuli were displayed on a 19'' Samsung SyncMaster 940BF LCD monitor. Stimulus representation and response recording were performed using Psychtoolbox-3 for Matlab 64-Bit (Version 3.0.14 - Build date: Apr 6th, 2018) running on a Windows operating system. Fixation was monitored with a desktop-mounted Eyelink 1000 eye-tracker (SR Research Ltd.) with a sampling frequency of 500 Hz or 1000 Hz and a spatial resolution of <0.01° RMS. Eye-movement data were down-sampled to 250 Hz. </p> </div> <div> <p><strong>Procedure for Handling Aborted Trials </strong></p> </div> <div> <p>If a participant's gaze fell outside the fixation window during the fixation period preceding the stimulus, or during stimulus presentation, the trial was aborted. Aborted trials were repeated at a randomly chosen time during the experiment. </p> </div> <div> <p><strong>Parametrization of the Experimental Setup </strong></p> </div> <div> <p>The eye-screen distance was set to 57 cm. The stimulus presentation time was set to 1000 ms. The inter-trial interval was set to 900 ms. Each session consisted of 30 blocks, with 25 trials per block. </p> </div> <div> <p><strong>Transfer Session </strong></p> </div> <div> <p>The transfer session (session 9) was unique in that the rectangular figure was presented in the upper left quadrant of the screen, rather than the lower right quadrant as in sessions 1-8. This was done to test the transfer of learning to a new location. Participants were made aware of the figure displacement but were not told in which quadrant to expect it. </p> </div> <div> <p><strong>Variables </strong></p> </div> <div> <p>The dataset includes identifiers for each participant (SubjectID), session (SessionID), and block of trials (BlockID). For each trial, the dataset includes the condition (Condition), the contrast heterogeneity (ContrastHeterogeneity), the grid coarseness (GridCoarseness), the participant's response (IndicatedOrientation), the actual orientation of the rectangular region (ActualOrientation), and whether the participant's response was correct (Correct). The dataset also includes demographic information comprising their unique identifier (SubjectID), age (Age), and sex (Sex). </p> <p><strong>Ethics</strong></p> <p>After receiving full information about all procedures and about the right to withdraw participation at any time, they provided written informed consent according to the Helsinki Declaration. All procedures were approved by the local Ethical Committee of the Faculty of Psychology and Neuroscience (ERCPN). Participants received<br>monetary reward.</p> </div> </div>
ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation
<p>There has been a recent surge in methods that aim to decompose and segment scenes into multiple objects in an unsupervised manner, i.e., unsupervised multi-object segmentation. Performing such a task is a long-standing goal of computer vision, offering to unlock object-level reasoning without requiring dense annotations to train segmentation models. Despite significant progress, current models are developed and trained on visually simple scenes depicting mono-colored objects on plain backgrounds. The natural world, however, is visually complex with confounding aspects such as diverse textures and complicated lighting effects. In this study, we present a new benchmark called ClevrTex, designed as the next challenge to compare, evaluate and analyze algorithms. ClevrTex features synthetic scenes with diverse shapes, textures and photo-mapped materials, created using physically based rendering techniques. ClevrTex has 50k examples depicting 3-10 objects arranged on a background, created using a catalog of 60 materials, and a further test set featuring 10k images created using 25 different materials. We benchmark a large set of recent unsupervised multi-object segmentation models on ClevrTex and find all state-of-the-art approaches fail to learn good representations in the textured setting, despite impressive performance on simpler data. We also create variants of the ClevrTex dataset, controlling for different aspects of scene complexity, and probe current approaches for individual shortcomings.</p> <p>Project webpage: https://www.robots.ox.ac.uk/~vgg/data/clevrtex/</p> <p>These are <strong>the variant datasets</strong>. Please see project page for links to the main dataset.</p>
ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation
<p>There has been a recent surge in methods that aim to decompose and segment scenes into multiple objects in an unsupervised manner, i.e., unsupervised multi-object segmentation. Performing such a task is a long-standing goal of computer vision, offering to unlock object-level reasoning without requiring dense annotations to train segmentation models. Despite significant progress, current models are developed and trained on visually simple scenes depicting mono-colored objects on plain backgrounds. The natural world, however, is visually complex with confounding aspects such as diverse textures and complicated lighting effects. In this study, we present a new benchmark called ClevrTex, designed as the next challenge to compare, evaluate and analyze algorithms. ClevrTex features synthetic scenes with diverse shapes, textures and photo-mapped materials, created using physically based rendering techniques. ClevrTex has 50k examples depicting 3-10 objects arranged on a background, created using a catalog of 60 materials, and a further test set featuring 10k images created using 25 different materials. We benchmark a large set of recent unsupervised multi-object segmentation models on ClevrTex and find all state-of-the-art approaches fail to learn good representations in the textured setting, despite impressive performance on simpler data. We also create variants of the ClevrTex dataset, controlling for different aspects of scene complexity, and probe current approaches for individual shortcomings.</p> <p>Project webpage: https://www.robots.ox.ac.uk/~vgg/data/clevrtex/</p> <p>This is the <strong>main dataset and OOD test set. </strong>Please see project page for links to the dataset variants.</p>
A Comparison of Different Textured and Non-Textured Anti-Reflective-Coatings for Planar Monolithic Silicon-Perovskite Tandem Solar Cells
<p>Figure data for the paper: A Comparison of Different Textured and Non-Textured Anti-Reflective-Coatings for Planar Monolithic Silicon-Perovskite Tandem Solar Cells. Submitted to ACS Applied Energy Materials.</p>
Tuning topological spin textures in size-tailored chiral magnet insulator particles
<p>The file contains the raw data and code used for the paper entitled "Tuning topological spin textures in size-tailored chiral magnet insulator particles" by Priya R. Baral et al. to be published in Journal of Physical Chemistry C. </p> <p>Requests for further information can be directed to the corresponding author: Arnaud Magrez (arnaud.magrez 'at' epfl.ch) </p>
Crystal size and texture data of sea ice from the Antarctic Marginal Ice Zone collected in Winter 2019
<p>This dataset details the crystal size and texture data from the sea ice cores collected during the SCALE Winter 2019 voyage to the Antarctic Marginal Ice Zone. Each sample collected has a series of accompanying photograph figures from which the crystal size and texture are based. </p>
Crystal size and texture data of sea ice from the Antarctic Marginal Ice Zone collected in Spring 2019
<p>This dataset details the crystal size and texture data from the sea ice cores collected during the SCALE Spring 2019 voyage to the Antarctic Marginal Ice Zone. Each sample collected has a series of accompanying photograph figures from which the crystal size and texture are based. </p>
Datasets for Lorentz electron ptychography towards sub-nanometer resolution imaging of magnetic textures
<p>These data sets are the raw experimental data used in a Letter titled, Lorentz electron ptychography for imaging magnetic textures beyond the diffraction limit published on Nature Nanotechnology. The related paper should be cited whenever the datasets are used.</p> <p>Reference:</p> <p>Zhen Chen, Emrah Turgut, Yi Jiang, Kayla X. Nguyen, Matthew J. Stolt, Song Jin, Daniel C. Ralph, Gregory D. Fuchs, David A. Muller, Lorentz electron ptychography for imaging magnetic textures beyond the diffraction limit. Nature Nanotechnology, in press, https://doi.org/10.1038/s41565-022-01224-y (2022).</p> <p>The file format is Matlab's *.mat file with version 7.3.</p> <p>The diffraction patterns are stored as the variable 'cbed'.</p> <p>Experimental conditions can be found in data_info.txt and the related paper.</p> <p> </p>
NUUR-Texture500: A Diverse Dataset of High Resolution Homogeneous Textures
<p>"NUUR-Texture500" is a dataset of diverse texture images created to facilitate research on texture analysis and synthesis. The textures in this dataset are spatially homogeneous, ranging from regular to stochastic, typically containing repeated elements with random variations in position, shape, orientation and color. For a detailed description of the dataset construction and contents, readers should refer to the following paper:</p> <p>Jue Lin, Gaurav Sharma, Thrasyvoulos N. Pappas, "Towards Universal Texture Synthesis by Combining Texton Broadcasting with Noise Injection in StyleGAN-2", Journal of e-Prime - Advances in Electrical Engineering, Electronics and Energy (3) (2023), https://doi.org/10.1016/j.prime.2022.100092</p> <p>Permission to copy and use this dataset for noncommercial use is hereby granted provided this notice is retained in all copies and the dataset distribution and the paper mentioned below are clearly cited.</p> <p>Contacts:<br> Jue Lin: jue.lin@u.northwestern.edu<br> Gaurav Sharma: gaurav.sharma@rochester.edu<br> Thrasyvoulos N. Pappas: pappas@ece.northwestern.edu</p> <p>Disclaimer: </p> <p>The dataset is provided "as is" with ABSOLUTELY NO WARRANTY expressed or implied. Use at your own risk.</p> <p>Acknowledgment: </p> <p>The NUUR-Texture500 texture images are curated from a number of publicly accessible sources. We acknowledge and thank the original sites for their contributions:<br> Flickr www.flickr.com<br> NeedPix www.needpix.com<br> Pexels www.pexels.com<br> PickUpImage www.pickupimage.com<br> Pixabay www.pixabay.com<br> PublicDomainPictures www.publicdomainpictures.net<br> RawPixel www.rawpixel.com<br> Unsplash www.unsplash.com<br> WikimediaCommons commons.wikimedia.org</p> <p> </p>
FIGURE 7 in Paleoecology of the Rhinocerotidae (Mammalia, Perissodactyla) from Béon 1, Montréal-du-Gers (late early Miocene, SW France): Insights from dental microwear texture analysis, mesowear, and enamel hypoplasia
FIGURE 7. Percentages of specimens above anisotropy (epLsar> 0.005) or complexity (Asfc> 2) cutpoints by species, facet, and preparation type. Triangles: living rhinoceros' species; circles: Béon 1 fossil rhinocerotids; size proportional to the number of specimens.
FIGURE 8 in Paleoecology of the Rhinocerotidae (Mammalia, Perissodactyla) from Béon 1, Montréal-du-Gers (late early Miocene, SW France): Insights from dental microwear texture analysis, mesowear, and enamel hypoplasia
FIGURE 8. Barplots of mesowear scores on permanent teeth by method (ScoreA, ScoreB, Ruler) and by species. A- ScoreA: mesowear score based on Winkler and Kaiser (2011); B- ScoreB: mesowear score adapted from Fortelius and Solounias (2000); C- Ruler: mesowear score based on Mihlbachler et al. (2011). Only one tooth per specimen was considered.
FIGURE 1 in Paleoecology of the Rhinocerotidae (Mammalia, Perissodactyla) from Béon 1, Montréal-du-Gers (late early Miocene, SW France): Insights from dental microwear texture analysis, mesowear, and enamel hypoplasia
FIGURE 1. Location map of Béon 1 locality, Montréal-du-Gers (MN4; mid-Orleanian, late early Miocene, south western France). The locality of Béon 1 is located (red circle) on the map of France (upper left corner) and on the zoom of south western France. Main cities (grey circles; bold) and rivers are indicated on the zoomed map. Dashed line represents the Spain-France frontier. Modified from Antoine and Duranthon (1997).
FIGURE 5 in Paleoecology of the Rhinocerotidae (Mammalia, Perissodactyla) from Béon 1, Montréal-du-Gers (late early Miocene, SW France): Insights from dental microwear texture analysis, mesowear, and enamel hypoplasia
FIGURE 5. Comparison of the DMTA patterns by species, facet and preparation type. Upper graphs: hand-prepared specimens; lower graphs: sand-prepared specimens. Left graphs: grinding facet; right graphs: shearing facet. Boxplots of anisotropy and complexity were plotted along with the dotplots to facilitate graph interpretation.
Fig. 1 in Infection of Anastrepha ludens (Diptera: Tephritidae) adults during emergence from soil treated with Beauveria bassiana under various texture, humidity, and temperature conditions
Fig. 1. Adult mortality of Anastrepha ludens infected with different concentrations of Beauveria bassiana conidia, afer emerging from treated soil. Different letters indicate significant differences among treatments based on 1-way ANOVA followed by the Tukey Honest Significant Difference test, P <0.05).
CPFEM Goss Texture
<pre>Stress-Strain Analysis of a Polycrystal with Goss Texture</pre>
CPFEM Goss Texture
<pre>Stress-Strain Analysis of a Polycrystal with Goss Texture</pre>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.