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85 results for “visual image”
Sharpening of Hierarchical Visual Feature Representations of Blurred Images
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Improved visualization of oral microbial consortia- Associated images
<p>These images are associated to the paper <strong>"Improved visualization of oral microbial consortia" published in the Journal of Dental Research.</strong></p> <p>Sample author: Tabita Ramirez Puebla</p> <p>Images show microbial consortia from human tongue dorsum biofilm.</p> <p>Imaged in a confocal microscope (Zeiss LSM 780) with a spectral detector (32 channels).</p> <p>Objective: Plan-Apochromat 63X; N.A. 1.4; Oil </p> <p>Pixel size: 0.07 um x 0.07 um</p> <p>Image size (pixels): 2048 x 2048</p> <p>Optical section : 1 micron</p> <p> </p> <p><strong>File descriptions</strong></p> <p><strong>TIFF files in Image5D format. Files resulted from linear unmixing performed with Zeiss ZEN algorithm (ZEN Black) or using the non-linear least-squares function in MATLAB. Individual fluorophore and autofluorescence channels are presented. </strong><br>Fig2A_5D<br>Fig2C_5D<br>Fig4_zstack_5D (zstack with 12 optical slices)</p> <p><strong>jpg files of pseudocolored images</strong><br>Fig2A_jpeg<br>Fig2B_jpeg<br>Fig2C_jpeg<br>Fig2D_jpeg<br>Fig3A_jpeg_stack_RGB_tif (stack of 257 optical slices RGB images in tif format)<br>Fig3A_Montage20x13_jpeg (Montage of 257 optical slices)<br>Fig3B_jpeg<br>Fig3C_jpeg<br>Fig3D_jpeg<br>Fig4A_xy (view of xy plane)<br>Fig4A_xz_orthogonal (view of xy plane -> orthogonal representation of 12 optical slices)<br>Fig4A_yz_orthogonal (view of yz plane -> orthogonal representation of 12 optical slices)<br>Fig4B_jpeg<br>Fig4C_jpeg</p> <p><strong>Representative Zeiss .czi (raw files from LSM780 confocal microscope)</strong><br>Fig2A_raw (original czi file)<br>Fig2C_raw (original czi file)</p>
Visualization of Marsh Grass Roots and Rhizomes by CT imaging: VCR salt marshes, summer 2012
Computer-aided tomography and image processing previously has been used to accurately and rapidly quantify coarse root mass in coastal wetlands (Davey et al. 2011. Ecological Applications). The data in this data base are being used to develop the technique to allow for resolution of fine roots. The contribution of Spartina alterniflora roots-and-rhizomes (hereafter, roots) to soil volume is measured in VCR mainland marsh soils. Soil cores were collected several different marshes with differing soil types (mineral vs peaty). The cores were scanned by computer-aided tomography and image processing was used to determine the volume of living roots. Our results show that CT imaging may also be used to quantify coarse and fine root volume in salt marsh soils and that in peaty soils, coarse roots make an important contribution to soil volume (up to 53% of the soil volume is live roots). The dataset includes an extensive manual of methods, along with instructional videos.
Dataset for the manuscript "Attribute Recognition: A New Method for Grouping Planetary Images by Visual Characteristics, Using the Example of Mn-Rich Rocks in the Floor of Gale Crater, Mars."
<p>This dataset supports the manuscript "Attribute Recognition: A New Method for Grouping Planetary Images by Visual Characteristics, Using the Example of Mn-Rich Rocks in the Floor of Gale Crater, Mars." The dataset is contained in a single CSV file with 201 data rows (one row per NASA Curiosity rover ChemCam instrument target used in the study). The columns in this dataset include the martian solar day (sol) on which each target was imaged by ChemCam; the standoff distance from ChemCam to each target (in meters); binary columns (values are either 1 or 0, indicating presence or absence, respectively) for each of the 17 visual attributes we documented for each target image; the corresponding greyscale ChemCam RMI mosaic file location (on the Planetary Data System); and columns indicating which group each target was sorted into under each classification algorithm discussed in the text (P_{SG}: simple graph method; P_{AP}: automatic partitioning method; P_{\lambda=1.6}: community detection method with \lambda=1.6). To obtain the binary strings used for the classification algorithms, the 17 visual attribute columns can be concatenated. </p> <p>Also included is a collection of HTML files that enables easy viewing of the RMI mosaics in each cluster, using the Planetary Data System links. To use it, download the <code>.zip</code> file, unzip it, and open the <code>index.html</code> file in the browser of your choice (likely will work to simply double-click <code>index.html</code>)</p>
Animal Recognition Using Methods Of Fine-Grained Visual Analysis - Kashtanka Pets (All Dev and Test Images, Single Folder)
<p>Kashtanka Pets images, with all Dev and Test images (total 66639 images). In a single folder, with filenames indicating path of file in original dataset distribution.</p>
Composite geostationary weather satellite images (second time derivative of water vapor channel) for visualizing Lamb waves
<p>Second time derivative of water vapor channel (6.2 micrometer) brightness temperature from geostationary weather satellites (Units: K s<sup>-2</sup>)</p> <p>Himawari-8 (original data obtained from NICT Science Cloud)</p> <p>GOES-16/17 (original data obtained from Amazon AWS)</p> <p>Meteosat-8/9/10/11 (original data obtained from EUMETSAT)</p> <p> </p> <p>Time interval of the files: 5 minutes</p> <p> </p> <p>Time interval of each satellite, dt for time derivative:</p> <p>Himaawri-8, GOES-16/17: 10 minutes, 10 minutes</p> <p>Meteosat-8/9/11: 15 minutes, 15 minutes</p> <p>Meteosat-10: 5 minutes, 10 minutes</p> <p> </p> <p>Each file contains the latest images from those satellites at that time. The time stamp for each satellite represents the beginning of each full-disk scan.</p> <p> </p> <p>Bias correction:</p> <p>Himawari-8: bias removal for each swath</p> <p>GOES-16/17, Meteosat-11: bias removal for each east-west line</p> <p>Meteosat-8/9/10: bias removal for each east-west line (note: satellite attitude was not stable)</p> <p> </p> <p>Smoothing:</p> <p>Band-pass filter for each full-disk image separately: 2-40 degrees on lat-lon coordinate</p> <p>Stronger smoothing at latitudes higher than 60 degrees north/south</p> <p> </p> <p>Down-sampling:</p> <p>Full-disk images were mapped to a 0.04-degree lat-lon coordinate.</p> <p>Then, composite images were produced at the 0.2-degree resolution.</p> <p> </p> <p>Version 2:</p> <p>Improved interpolation algorithm</p> <p>Himawari-8: improved geolocation</p> <p>Meteosat-8/9: improved treatment of noise near the edge of full disk images</p>
Images and results from a visual inspection of AIA spikes
<p>This upload contains images and results used in a manuscript submitted by P.R. Young et al. to the Solar Physics journal. The preprint is available at: <a href="https://ui.adsabs.harvard.edu/abs/2021arXiv210802624Y/abstract">https://ui.adsabs.harvard.edu/abs/2021arXiv210802624Y/abstract</a>.</p> <p>The files are:</p> <p>RESULTS.txt - The results of the spike analysis.<br> make_spike_images.pro - An IDL routine for generating the pdf files<br> (requires software in the Solarsoft IDL<br> distribution to run)<br> spike_images_*_*.pdf - A set of pdf files containing AIA images. For<br> each spike there is a pair of images in a<br> row. Each image is 30" x 30" in size and<br> centered on the spike. The left panel shows<br> the original image, and the right panel shows<br> the despiked image. There are 45 pdf files in<br> all. </p> <p> </p>
Examples of ancient Near Eastern artifacts imaged and visualized with PLD system
<p><strong>From top to bottom: a coin, a cylinder seal impression, impressions on the bottom of a funerary cone, and a cuneiform tablet. For each artifact four visualizations were generated with the PLD MLR viewer. From left to right: coloor, shaded, automated sketch, normal map. References for the objects: Greek silver coin: o.i. 522 (©️ KU Leuven Art Collection); Modern impression Old Akkadian cylinder seal: O.861 (©️ Art & History Museum, Brussels - RMAH); Old Egyptian funerary cone: E.3984 (©️ Art & History Museum, Brussels); Old Akkadian cuneiform tablet: O.95 (©️ Art & History Museum, Brussels).</strong></p>
A three-photon head-mounted microscope for imaging all layers of visual cortex in freely moving mice
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Developmentally stable representations of naturalistic image structure in macaque visual cortex
<p>Data and analysis code used in <em>Developmentally stable representations of naturalistic image structure in macaque visual cortex</em></p> <div>Gerick M. Lee, C. L. Rodríguez-Deliz, Brittany N. Bushnell, Najib J. Majaj, J. Anthony Movshon, Lynne Kiorpes</div> <div>bioRxiv 2024.02.24.581889; doi: https://doi.org/10.1101/2024.02.24.581889</div> <p>The directory structure includes three .m files, each beginning with the word "repository" - these three files should work out of the box to generate all the figures from the manuscript. </p> <p>1) Download the zip file.</p> <p>2) Navigate to the repository directory in matlab.</p> <p>3) Run the repository of interest.</p>
SubDiv17: A Dataset for Investigating Subjectivity in the Visual Diversification of Image Search Results
<p>This dataset facilitates the comparison of approaches aiming at the diversification of image search results. The dataset was explicitly designed for general-purpose, multi-topic queries and provides multiple ground truth annotations to allow for the exploration of the subjectivity aspect in the general task of diversification. The dataset provides images and their metadata retrieved from Flickr for around 200 complex queries. Additionally, to encourage experimentations (and cooperations) from different communities such as information and multimedia retrieval, a broad range of pre-computed descriptors is provided. The dataset was successfully validated during the MediaEval 2017 Retrieving Diverse Social Images task using 29 submitted runs. For more information, please see <a href="https://doi.org/10.1145/3204949.3208122">https://doi.org/10.1145/3204949.3208122</a>.</p>
Supporting data for the EIAS 2023 Image Data Visualization workshop
<p>A collection of scientific images used for demonstration purposes in the Image Data Visualization workshop given in the EPFL EIAS 2023 summer school by the EPFL Center for Imaging.</p> <p>The images are automatically downloaded and used in the code repository <a href="https://gitlab.epfl.ch/center-for-imaging/eias-2023-visualization-workshop">Image Data Visualization with Python and Napari</a> on GitLab.</p> <p>The original provenance of the images is summarized below.</p> <table> <tbody> <tr> <td>cell_tracking_2d.tif</td> <td><a href="http://celltrackingchallenge.net/3d-datasets/">Cell Tracking Challenge</a></td> </tr> <tr> <td>deepslide.png</td> <td><a href="https://zenodo.org/record/1184621">DeepSlides dataset</a></td> </tr> <tr> <td>drosophila_trachea.tif</td> <td>Provided by the <a href="https://www.epfl.ch/labs/lemaitrelab/">Lemaitre lab</a> in EPFL.</td> </tr> <tr> <td>lungs_ct.tif</td> <td>Provided by <a href="https://www.epfl.ch/labs/depalma-lab/">Prof. De Palma's lab</a> in EPFL.</td> </tr> <tr> <td>snow_3d.tif</td> <td>Example data from the Python <a href="https://ttk.gricad-pages.univ-grenoble-alpes.fr/spam/index.html">spam</a> package.</td> </tr> <tr> <td>crystallites.tif</td> <td>Provided by the <a href="https://www.epfl.ch/labs/las/">LAS</a> lab in EPFL</td> </tr> </tbody> </table>
Calcium imaging data from: Functional organization of visual responses in the octopus optic lobe
<p>Cephalopods are highly visual animals with camera-type eyes, large brains, and a rich repertoire of visually guided behaviors. However, the cephalopod brain evolved independently from that of other highly visual species, such as vertebrates, and therefore the neural circuits that process sensory information are profoundly different. It is largely unknown how their powerful but unique visual system functions, since there have been no direct neural measurements of visual responses in the cephalopod brain. In this study, we used two-photon calcium imaging to record visually evoked responses in the primary visual processing center of the octopus central brain, the optic lobe, to determine how basic features of the visual scene are represented and organized. We found spatially localized receptive fields for light (ON) and dark (OFF) stimuli, which were retinotopically organized across the optic lobe, demonstrating a hallmark of visual system organization shared across many species. Examination of these responses revealed transformations of the visual representation across the layers of the optic lobe, including the emergence of the OFF pathway and increased size selectivity. We also identified asymmetries in the spatial processing of ON and OFF stimuli, which suggest unique circuit mechanisms for form processing that may have evolved to suit the specific demands of processing an underwater visual scene. This study provides insight into the neural processing and functional organization of the octopus visual system, highlighting both shared and unique aspects, and lays a foundation for future studies of the neural circuits that mediate visual processing and behavior in cephalopods.</p>
Calcium imaging data from: Functional organization of visual responses in the octopus optic lobe
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Example datasets for ScopeViewer: A Browser-Based Solution for Visualizing Large Biological Images
<p>This is an example dataset.</p><p>It accompanies the manuscript titled "ScopeViewer: A Browser-Based Solution for Visualizing Large Biological Images".</p>
Estimating density of mountain hares using distance sampling: a comparison of daylight visual surveys, night-time thermal imaging and camera traps
<p><a name="_Hlk58254629"></a></p> <p><a name="_Hlk58254629">Surveying cryptic, nocturnal animals is logistically challenging. Consequently, density estimates may be imprecise and uncertain. Survey innovations mitigate ecological and observational difficulties contributing to estimation variance. Thus, comparisons of survey techniques are critical to evaluate estimates of abundance. We simultaneously compared three methods for observing mountain hare (<i>Lepus timidus</i>) using Distance sampling to estimate abundance. Daylight visual surveys achieved 41 detections, estimating density at 14.3 hares km<sup>-2</sup> (95%CI 6.3–32.5) resulting in the lowest estimate and widest confidence interval. Night-time thermal imaging achieved 206 detections, estimating density at 12.1 hares km<sup>-2 </sup>(95%CI 7.6–19.4). Thermal imaging captured more observations at furthest distances, and detected larger group sizes. Camera traps achieved 3,705 night-time detections, estimating density at 22.6 hares km<sup>-2 </sup>(95%CI 17.1–29.9). Between the methods, detections were spatially correlated, although the estimates of density varied. Our results suggest that daylight visual surveys tended to underestimate density, failing to reflect nocturnal activity. Thermal imaging captured nocturnal activity, providing a higher detection rate, but required fine weather. Camera traps captured nocturnal activity, and operated 24/7 throughout harsh weather, but needed careful consideration of empirical assumptions. </a>We discuss the merits and limitations of each method with respect to the estimation of population density in the field.</p>
Water stages in a tidal marsh measured using images, visually and automatically, along with stages measured using pressure transducer and Doppler sensors
<p>This data was acquired to evaluate the performance of an image based system to measure water stages in streams and rivers.</p> <p>It contains measurements performed visually and stored (NR_Visual_Data_120201_120724.csv)</p> <p>It contains the measurements performed automatically by the system studied corresponding to those done visually (NR_Gaugecam_Data_120201_120724.csv)</p> <p>It contains the measurements done automatically (GC) and those measured by ISCO, HOBO, and Sontek instruments (NR-GC-vs-ISCO.csv)</p>
Animal Recognition Using Methods Of Fine-Grained Visual Analysis - Kashtanka Pets (400 Hand-labelled Images - Cats & Dogs, Single Folder)
<p>400 images (200 cats, 200 dogs) hand-labelled by Maria E. with head and body bounding box labels in YOLOv5 format. Images are in a single folder, no separate folders for cats and dogs.</p>
Animal Recognition Using Methods Of Fine-Grained Visual Analysis - Kashtanka Pets (200 Hand-labelled Images, Cats and Dogs, Separate Folders)
<p>400 images (200 cats, 200 dogs) hand-labelled by Maria E. with head and body bounding box labels in YOLOv5 format. Images for cats, for dogs are in a separate folders.</p>
Laboratory visualization of fault asymmetry formation via acoustic emission and digital imaging correlation
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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.