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278 results for “stereo”
Data from: A novel stereo-video method to investigate fish-habitat relationships
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Validating the use of stereo-video cameras to conduct remote measurements of sea turtles
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Morphology of nares associated with stereo-olfaction in baleen whales
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Data from: Sharpening coarse-to-fine stereo vision by perceptual learning: asymmetric transfer across the spatial frequency spectrum
Neurons in the early visual cortex are finely tuned to different low-level visual features, forming a multi-channel system analysing the visual image formed on the retina in a parallel manner. However, little is known about the potential 'cross-talk' among these channels. Here, we systematically investigated whether stereoacuity, over a large range of target spatial frequencies, can be enhanced by perceptual learning. Using narrow-band visual stimuli, we found that practice with coarse (low spatial frequency) targets substantially improves performance, and that the improvement spreads from coarse to fine (high spatial frequency) three-dimensional perception, generalizing broadly across untrained spatial frequencies and orientations. Notably, we observed an asymmetric transfer of learning across the spatial frequency spectrum. The bandwidth of transfer was broader when training was at a high spatial frequency than at a low spatial frequency. Stereoacuity training is most beneficial when trained with fine targets. This broad transfer of stereoacuity learning contrasts with the highly specific learning reported for other basic visual functions. We also revealed strategies to boost learning outcomes 'beyond-the-plateau'. Our investigations contribute to understanding the functional properties of the network subserving stereovision. The ability to generalize may provide a key principle for restoring impaired binocular vision in clinical situations.
Confronting Large-Eddy Simulations with Stereo Camera Data by means of reconstructed hemispheric Cloud Size Distributions
<p>Dataset to produce the results of the publication: "<em>Confronting Large-Eddy Simulations with Stereo Camera Data by means of reconstructed hemispheric Cloud Size Distributions</em>". This dataset supports the findings presented in the publication and includes comprehensive resources for replicating its analysis and visualization. </p> <p> The dataset encompasses:</p> <ul> <li><strong>Dutch Atmospheric Large-Eddy Simulation (DALES) Data</strong><br> <ul> <li>Configuration files</li> <li>Selected simulation output data</li> </ul> </li> <li><strong>Image Data</strong> <ul> <li>Rendered stereo camera images from the DALES output</li> <li>Actual stereo camera images</li> <li>Cloud masks generated from these images</li> </ul> </li> <li><strong>Camera-Based Reconstructions</strong> <ul> <li>Reconstructed cloud fields from the rendered camera images</li> <li>Reconstructed cloud fields from the actual camera images</li> </ul> </li> <li><strong>Derived Cloud Metrics</strong> <ul> <li>Cloud base areas, cloud base heights, and cloud cover from the camera-based reconstructions</li> </ul> </li> <li><strong>Observational Data</strong> <ul> <li>Radiosondes, Ceilometer, and Cloudnet measurements</li> <li>Cloud cover from radiation measurements</li> <li>Mixed layer height from the Doppler lidar</li> </ul> </li> <li><strong>Reproduction Scripts</strong> <ul> <li>Scripts to reproduce the analysis and figures</li> </ul> </li> </ul> <p> </p> <p>This research was supported by the U.S. Department of Energy's Atmospheric System Research, an Office of Science Biological and Environmental Research program, under grant DE-SC0022126 and by the German Research Foundation (DFG) under project number 430226822 (https://gepris.dfg.de/gepris/projekt/430226822). The Gauss Centre for Supercomputing e.V. (https://www.gauss-centre.eu/) is acknowledged for providing computing time on the Gauss Centre for Supercomputing (GCS) supercomputer JUWELS at the Jülich Supercomputing Centre (JSC) under the projects RCONGM and VIRTUALLAB. JOYCE data were provided by the Institute for Geophysics and Meteorology of the University of Cologne. JOYCE is a collaborative research platform between University of Cologne and Forschungszentrum Jülich within the European research infrastructure ACTRIS. We acknowledge ACTRIS and the Finnish Meteorological Institute for providing Cloudnet data which is available for download from https://cloudnet.fmi.fi. We acknowledge ECMWF for providing IFS model data.</p>
Mouse Stereo-seq - processed data
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AnoVox_Static_Stereo_Town02
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AnoVox_Dynamic_Stereo_Town04
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AnoVox_Static_Stereo_Town03
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AnoVox_Static_Stereo_Town05
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AnoVox_Static_Stereo_Town01
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AnoVox_Dynamic_Stereo_Town10
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AnoVox_Static_Stereo_Town10
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AnoVox_Static_Stereo_Town07
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AnoVox_Static_Stereo_Town04
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AnoVox_Static_Stereo_Town06
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AnoVox_Normality_Stereo_Town04
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AnoVox_Normality_Stereo_Town10
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SQUID- Stereo Quantitative Underwater Image Dataset
<p>Underwater Single Image Color Restoration</p> <p>SQUID- Stereo Quantitative Underwater Image Dataset</p> <p><a href="http://www.eng.tau.ac.il/~berman">Dana Berman</a>, Deborah Levy, <a href="http://www.eng.tau.ac.il/~avidan/">Shai Avidan, </a><a href="https://www.viseaon.haifa.ac.il/">Tali Treibitz</a></p> <p> </p> <p>Abstract</p> <p>Underwater images suffer from color distortion and low contrast, because light is attenuated while it propagates through water. Attenuation under water varies with wavelength, unlike terrestrial images where attenuation is assumed to be spectrally uniform. The attenuation depends both on the water body and the 3D structure of the scene, making color restoration difficult. Unlike existing single underwater image enhancement techniques, our method takes into account multiple spectral profiles of different water types. By estimating just two additional global parameters: the attenuation ratios of the blue-red and blue-green color channels, the problem is reduced to single image dehazing, where all color channels have the same attenuation coefficients. Since the water type is unknown, we evaluate different parameters out of an existing library of water types. Each type leads to a different restored image and the best result is automatically chosen based on color distribution. We collected a dataset of images taken in different locations with varying water properties, showing color charts in the scenes. Moreover, to obtain ground truth, the 3D structure of the scene was calculated based on stereo imaging. This dataset enables a quantitative evaluation of restoration algorithms on natural images and shows the advantage of our method.</p> <p> </p> <p>Publication</p> <p>The paper is availbale on <a href="https://arxiv.org/abs/1811.01343">arXiv</a>.</p> <p>If you use this dataset please cite it as SQUID [ref].<br> [Ref] Berman, Dana, Deborah Levy, Shai Avidan, and Tali Treibitz. "Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset." IEEE Transactions on Pattern Analysis and Machine Intelligence (2020).<br> <br> Bibtex entry:<br> @article{berman2020underwater,<br> title={Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset},<br> author={Berman, Dana and Levy, Deborah and Avidan, Shai and Treibitz, Tali},<br> journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},<br> year={2020}<br> }</p> <p>Source Code</p> <p>The code is availbale on GitHub: <a href="https://github.com/danaberman/underwater-hl">https://github.com/danaberman/underwater-hl</a>.</p> <p>Dataset</p> <p>The dataset includes RAW images, TIF files, camera clibration files, and distance maps.<br> The database contains 57 stereo pairs from four different sites in Israel, two in the Red Sea (representing tropical water) and two in the Mediterranean Sea (temperate water). In the Red Sea the sites were a coral reef ('Katzaa') which is 10-15 meters deep (15 pairs) and a shipwreck ('Satil'), 20-30 meters deep (8 pairs). In the Mediterranean Sea both sites were rocky reef environments, separated by 30km, Nachsholim at 3-6 meters depth (13 pairs), and Mikhmoret at 10-12 meters depth (21 pairs).<br> <br> For convenience it is divided to the 4 dive sites.<br> <br> <a href="http://csms.haifa.ac.il/profiles/tTreibitz/datasets/ambient_forwardlooking/files/README.md">README file</a>.<br> If you use this data, please <a href="http://csms.haifa.ac.il/profiles/tTreibitz/datasets/ambient_forwardlooking/underwater_haze_lines_and_dataset.bib">cite the paper</a>.<br> To evaluate your own results, please use this <a href="http://csms.haifa.ac.il/profiles/tTreibitz/datasets/ambient_forwardlooking/files/underwater-dataset-evaluation-undisorted-params.zip">evaluation code</a>.</p> <p><br> </p> <p>References</p> <p><strong>[Drews et al. 2013]</strong> P. Drews, E. Nascimento, F. Moraes, S. Botelho, and M. Campos. Transmission estimation in underwater single images. In <em>Proc. IEEE ICCV Underwater Vision Workshop, pages 825–830,</em> 2013.<br> <strong>[Peng et al. 2015]</strong> Y.-T. Peng, X. Zhao, and P. C. Cosman. Single underwater image enhancement using depth estimation based on blurriness. In <em>Proc. IEEE ICIP</em>, 2015.<br> <strong>[Ancuti et al. 2016]</strong> C. Ancuti, C. O. Ancuti, C. De Vleeschouwer, R. Garcia, and A. C. Bovik. Multi-scale underwater descattering. In <em>Proc. ICPR</em>, 2016.<br> <strong>[Ancuti et al. 2017]</strong> C. O. Ancuti, C. Ancuti, C. De Vleeschouwer, L. Neumann, and R. Garcia. Color transfer for underwater dehazing and depth estimation. In <em>Proc. IEEE ICIP</em>, 2017<em>(All color transfers were done with a single image)</em>.<br> <strong>[Emberton et al. 2017]</strong>S. Emberton, L. Chittka, and A. Cavallaro, Underwater image and video dehazing with pure haze region segmentation, <em>Computer Vision and Image Understanding</em>, 2017.<br> <strong>[Ancuti et al. 2018]</strong> . O. Ancuti, C. Ancuti, C. De Vleeschouwer, and P. Bekaert. Color balance and fusion for underwater image enhancement. IEEE Transactions on Image Processing, 27(1):379–393, 2018.</p>
OSCAR: Occluded Stereo dataset for Convolutional Architectures with Recurrence
<p><strong>OSCAR</strong>, the Occluded Stereo dataset for Convolutional Architectures with Recurrence. Version: 2.0<br> (dataset as presented in our JOV 2021 journal publication "Recurrent Processing Improves Occluded Object Recognition and Gives Rise to Perceptual Hysteresis")</p> <p>If you make use of the dataset, please cite as follows:</p> <p><strong>Ernst, M. R., Burwick, T., & Triesch, J. (2021). Recurrent Processing Improves Occluded Object Recognition and Gives Rise to Perceptual Hysteresis. In Journal of Vision</strong></p> <p>Contents</p> <ul> <li>readme.md - detailed description and sample pictures</li> <li>img.zip - folder that contains images for the readme file</li> <li>licence.md - licence agreement for using the datasets</li> <li>os-fmnist2c.zip - compressed archive of the occluded stereo FashionMNIST dataset (centered, ~1.1GB)</li> <li>os-fmnist2r.zip - compressed archive of the occluded stereo FashionMNIST dataset (random, ~1.2GB) </li> <li>os-mnist2c.zip - compressed archive of the occluded stereo MNIST dataset (centered, ~865MB)</li> <li>os-mnist2r.zip - compressed archive of the occluded stereo MNIST dataset (random, ~851MB)</li> <li>os-ycb2.zip - compressed archive of the occluded stereo ycb-object dataset (~1.1GB)</li> <li>os-ycb2_highres.zip - compressed archive of the occluded stereo ycb-object dataset (high resolution, ~9.8GB)</li> <li>OSCARv2_dataset.py - python script to directly load image data from folder, pytorch dataset</li> </ul>
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.