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69 results for “image sequence”
Dataset for Fisher et al. (2023). Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361.
<p>This dataset contains the video clips used to produce the results presented in:</p><p>Fisher, M., French, G., Gorpincenko, A., Holah, H., Clayton, L., Skirrow, R. and Mackiewicz, M., 2023. Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361.</p>
Supplementary data for: Comparison of optical flow derivation techniques for retrieving tropospheric winds from satellite image sequences
<p>This study introduces a validation technique for quantitative comparison of algorithms which retrieve winds from passive detection of cloud- and water vapor-drift motions, also known as Atmospheric Motion Vectors (AMVs). The technique leverages airborne wind-profiling lidar data collected in tandem with 1-min refresh rate geostationary satellite imagery. AMVs derived with different approaches are used with accompanying numerical weather prediction model data to estimate the full profiles of lidar-sampled winds which enables ranking of feature tracking, quality control, and height-assignment accuracy and encourages meso-scale, multi-layer, multi-band wind retrieval solutions. The technique is used to compare the performance of two brightness motion, or "optical flow," retrieval algorithms used within AMVs, 1) Patch Matching (PM; used within operational AMVs) and 2) an advanced Variational Optical Flow (VOF) method enabled for most atmospheric motions by new-generation imagers. The VOF AMVs produce more accurate wind retrievals than the PM method within the benchmark in all imager bands explored. It is further shown that image regions with low texture and multi-layer-cloud scenes in visible and infrared bands are tracked significantly better with the VOF approach, implying VOF produces representative AMVs where PM typically breaks down. It is also demonstrated that VOF AMVs have reduced accuracy where the brightness texture does not advect with the mean wind (e.g. gravity waves), where the image temporal noise exceeds the natural variability, and when the height-assignment is poor. Finally, it is found that VOF AMVs have improved performance when using fine-temporal refresh rate imagery, such as 1-min versus 10-min data.</p>
Figure 5. Forward walking image sequence-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>The behavior module determines the target position and orientation according to the results<br> of localization and the sensor measurements, and then constructs an action series which consists of<br> the elementary gaits to realize omni directional walking. The implementation of forward walking is<br> applying Virtual Slope Walking in the sagittal plane with the Lateral Swing Movement for lateral<br> stability. The sideward walking and turning is realized by carefully designing the key frames. All of<br> above gait is generated by connecting the key frames with smooth sinusoids. The forward walking<br> speed of PERSIA Humanoid Robot is 25cm/s. The image sequences of forward walking are shown<br> in Figure 5.</p>
Time-lapse (4D) volumetric fluorescence microscopy image sequence of a living zebrafish embryo
<p>The dataset contains a time-lapse (4D) volumetric fluorescence microscopy image sequence of a living zebrafish embryo (cxcr4aMO). The sequence has been captured with a confocal laser-scanning microscope during zebrafish gastrulation and shows endodermal cells that have been fluorescently labelled.</p> <p>The sequence is best viewed with Fiji (https://fiji.sc/) and can be loaded into Matlab with tiffread.m (http://www.cytosim.org/misc/index.html).</p> <p>For the treatment of the specimen see:</p> <p>S. Nair and T. F. Schilling. Chemokine signaling controls endodermal migration during zebrafish gastrulation. Science, 322(5898):89–92, October 2008.</p>
Microscopy image sequences and annotated kymographs of laser ablation experiments in Drosophila embryos
<p><strong>Content</strong></p> <p>This dataset contains 15 2D time-lapse fluorescence microscopy image sequences recorded with confocal laser-scanning microscopy. Each movie shows an epithelial tissue laser nanoablation experiment conducted in a Drosophila embryo.</p> <p>For each sequence, the dataset contains kymographs (one-dimensional space-time plots) of a supracellular cable that is cut during the ablation, and manually created tracks of visible features, such as the resulting cut ends. These tracks allow to estimate, for instance, recoil velocities of the cut tissue and may be used to evaluate automated methods for estimating said velocities.</p> <p>This dataset is used in the manuscript to evaluate various variational approaches for joint motion estimation and source identification:</p> <p>L. F. Lang, N. Dutta, E. Scarpa, B. Sanson, C.-B. Schönlieb, and J. Étienne. Joint Motion Estimation and Source Identification using Convective Regularisation with an Application to the Analysis of Laser Nanoablations. 2019.</p> <p><strong>Description</strong></p> <p>The movies depict a square region of approximately <span class="math-tex">\(42.2 \times 42.2 \, \mathrm{\mu m}^{2}\)</span> at a spatial resolution of <span class="math-tex">\(250 \times 250\)</span> pixels. A typical sequence contains between 60 and 100 frames. They temporal interval between recorded frames was <span class="math-tex">\(727.67 \, \mathrm{ms}\)</span>.</p> <p>Each sequence features cell membranes labelled with E-cadherin:GFP and shows a single plasma-induced laser nanoablation. The destructed tissue region is roughly of <span class="math-tex">\(2 \, \mathrm{\mu m}\)</span> length. This ablation is expected to have a width of the order of the size of one pixel. During the ablation the acquisition is paused, resulting in a black image.</p> <p>For the used microscopy techniques and for the preparation of flies, as well as for the details of the laser ablation method, see the paper:</p> <p>E. Scarpa, C. Finet, G. B. Blanchard, and B. Sanson. Actomyosin-driven tension at compartmental boundaries orients cell division independently of cell geometry In Vivo. Dev. Cell, 47(6):727–740.e6, December 2018. URL: <a href="https://doi.org/10.1016/j.devcel.2018.10.029">https://doi.org/10.1016/j.devcel.2018.10.029</a></p> <p>The kymographs and the manually created annotations (tracks) of features were created using Fiji (<a href="https://fiji.sc/">https://fiji.sc/</a>).</p> <p><strong>Content</strong></p> <p>The dataset contains 15 sequences placed in the following folder structure:</p> <ul> <li>SqAX3_SqhGFP42_GAP43_TM6B <ul> <li>190216E4PSB1</li> <li>190216E5PSB1</li> <li>190216E5PSB2</li> <li>190216E6PSB1</li> <li>190216E8PSB1</li> <li>E2PSB1</li> <li>E5PSB2</li> <li>E8PSB1</li> <li>PSB1E1</li> <li>PSB4</li> </ul> </li> <li>SqhGFP40 <ul> <li>e1_PSB8</li> <li>e3_PSB9</li> <li>e3_PSB10</li> <li>e4_PSB11</li> <li>e4_PSB12</li> </ul> </li> </ul> <p>Each folder contains:</p> <ul> <li>The sequence itself in TIF format, e.g. "190216E4PSB1PMT - PMT [560-] _C1.ome.tif".</li> <li>A file "reslice.roi" that indicates the location/direction of the cut supracellular cable.</li> <li>3 different kymographs for each sequence obtained by taking avg/max/sum projections in Fiji orthogonal to the line specified in "reslice.roi", e.g. <ul> <li>"AVG_Reslice of 190216E4PSB1PMT.tif",</li> <li>"MAX_Reslice of 190216E4PSB1PMT.tif",</li> <li>"SUM_Reslice of 190216E4PSB1PMT.tif".</li> </ul> </li> <li>Text files that state the time/space coordinates of manually tracked features in the kymographs, e.g. <ul> <li>"cutend_L.txt" (coordinates of the left cut end after the ablation),</li> <li>"cutend_R.txt" (coordiantes of the right cut end),</li> <li>"feat_X.txt" (coordinates of additional features, where X is a number and L or R).</li> </ul> </li> <li>A ZIP file "manual_ROIs.zip" that contains all the coordinates of tracked features of the kymograph in ROI format (e.g. "cutend_L.roi").</li> </ul> <p><strong>Usage</strong></p> <p>The sequences, kymographs, and the tracks can be viewed using, for example, Fiji.</p> <p>For the automated analysis, see the Python code that accompanies the manuscript above. It is available at https://dx.doi.org/XXX</p> <p><strong>License information</strong></p> <p>This dataset is released under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. See <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NA-SC 4.0</a>.</p> <p><strong>How to cite this dataset</strong></p> <p>If you use this dataset in an academic publication, please consider citing the paper:</p> <p>L. F. Lang, N. Dutta, E. Scarpa, B. Sanson, C.-B. Schönlieb, and J. Étienne. Joint Motion Estimation and Source Identification using Convective Regularisation with an Application to the Analysis of Laser Nanoablations. 2019.</p> <p>To cite solely the dataset, please use:</p> <p>L. F. Lang, N. Dutta, E. Scarpa, B. Sanson, C.-B. Schönlieb, and J. Étienne. (2019). Microscopy image sequences and annotated kymographs of laser ablation experiments in Drosophila embryos [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.3257654">http://doi.org/10.5281/zenodo.3257654</a></p>
Programmatic Image Sequences for Multi-Disciplinary Applications: 16 Bit TIFF Sequences, HDR FULL RANGE
<p>This dataset is a part of the paper "Defining and Characterizing Programmatic Image Sequences for Multi-Disciplinary Applications". This download is an extended part of the "Spatio-Temporal Noise Sequences: Multipurposed Pseudo-Random Visual Test Signals" project. The main project for this can be found on GitHub <a href="https://github.com/FloFriedrich/STnoise">https://github.com/FloFriedrich/STnoise</a></p>
Representative snapshots of imaging sequences used in transcan_IDH-mutant_LGG_radiomics analysis
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Supplementary data for: Comparison of optical flow derivation techniques for retrieving tropospheric winds from satellite image sequences
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Fluorescent (C)LSM image sequences of Dictyostelium discoideum (Ax2 - LifeAct mRFP) for cell track and cell contour analysis
<p>This data set is designed for cell contour and cell track analysis. Hence image sequences of moving Dictyostelium discoideum cells are recorded. For the purpose to facilitate the detection of the cell contour we take fluorescent images of the cortical protein actin to obtain a high contrast between background and cell body. In each image sequences several cells are recorded. This allows for an analysis of all cells at once or to crop single cell tracks.</p>
Entire confocal z-stack series as .tif image sequences
<p>The manuscript entitled "Parvalbumin-expressing ependymal cells in rostral lateral ventricle wall adhesions contribute to aging-related ventricle stenosis in mice" shows confocal z-stack maximum intensity projections and thin z-plane reconstructions in the figure plates. The entire confocal z-stack image series are provided here as .tif image sequences, respectively the confocal z-stacks of the negative controls as well. The file names refer to the figure numbers and position in the figure plates. For more information about the immunostaining and image acquisition, see the Materials & Methods and Figure legends in the manuscript.</p>
Image Sequence 2021
<p>Images of fluorescing C+4 ions at a several velocities and</p> <p>times. (a) and (b) show the same velocity bins at two different times.</p> <p>(c) and (d) show the same time at two different velocity bins.</p>
Sequences of Orthorectified Images of the Water Surface of Two Rivers: River Sheaf (Sheffield, UK), and River Calder (Todmorden, UK)
<p>This data set contains sequences of orthorectified images of the free surface of two rivers: </p> <ul> <li>River Sheaf, Sheffield, United Kingdom (Latitude: 53.373056° Longitude: -1.463913° (WGS 84)), recorded between January 2019 and February 2020;</li> <li>River Calder, Todmorden, United Kingdom (Latitude: 53.716198° Longitude: -2.097028°), recorded between 6 and 13 October 2020.</li> </ul> <p>The images are extracted from videos of the water surface also included in the data set. They are complemented by Matlab files containing the corresponding average space-time Fourier spectra. Each measurement is associated with the corresponding measurement of the river depth and estimates of the discharge.</p> <p>Please refer to Documentation.pdf for further details.</p>
Figure 6. Forward walking image sequence-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>The image sequences of sideward walking and turning are shown in Figure 6 respectively.</p>
Tif Image Stack - Example of a long sequence that was split into two tif files
<p>These two tif files were recorded during a single time-series capture. </p> <p>20240723_uro3g_t000000.tif - This image stack contains 3984 frames (time slices)</p> <p>20240723_uro3g_t000001.tif - This image stack contains 324 frames (time slices).</p> <p>The total number of frames/time slices should be 4308. (3984 + 324)</p> <p> </p> <p> </p> <p> </p>
Phantom measurement data for 'Myelin water imaging at 0.55 T using a multi-gradient-echo sequence'
<p>This dataset contains the phantom mGRE measurement data used in the submitted article Schäper et al. 'Myelin water imaging at 0.55 T using a multi-gradient-echo sequence'.<br> The acquisitions were done with a 0.55 T MRI system (Magnetom Free.Max; Siemens Healthcare, Erlangen, Germany) using a 12 channel head coil and a 3 T MRI system (Magnetom Prisma; Siemens Healthcare, Erlangen, Germany) using a 20 channel head coil. For further measurement details, please refer to the mentioned original article.</p> <p> </p>
Generation of synthetic whole-slide image tiles of tumours from RNA-sequencing data via cascaded diffusion models
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Single molecule image sequences of Thermothielavioides terrestris AA9E (TtAA9E) on cellulose in oxygen scavenging systems
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Fluorescent (C)LSM image sequences of Dictyostelium discoideum (Ax2 - LifeAct mRFP) for cell track and cell contour analysis
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Multi-Domain Translation between Single-Cell Imaging and Sequencing Data using Autoencoders
<p>This record contains raw data related to the article "Multi-Domain Translation between Single-Cell Imaging and Sequencing Data using Autoencoders".</p>
Spatial transcriptome mapping of the desmoplastic growth pattern of colorectal liver metastases by in situ sequencing - Image and In SItu Sequencing data
<p>Image and ISS data for Spatial transcriptome mapping of the desmoplastic growth pattern of colorectal liver metastases by in situ sequencing reveals a biologically relevant zonation of the desmoplastic rim.</p><p>Image data consists of:</p><ul><li>Nucler stain (DAPI)</li><li>Masks for liver, rim and tumor regions</li><li>H&E images of parallel tissue sections</li></ul><p>Gene and cluster marker data is collected in the <i>markers.h5ad</i> file which can be read using AnnData (<a href="https://anndata.readthedocs.io/en/latest/">https://anndata.readthedocs.io/en/latest/)</a>. </p><p> </p>
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.