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1,832 results for “Cameras”
Old Camera
For more information about this item visit: https://bhpsite.org/ Source: Objaverse 1.0 / Sketchfab
Druh still camera, model II
The purpose of a still camera is to record images of objects in space on light-sensitive materials: glass plates, light-sensitive film, or digitally. The first small-frame photographic camera, the Leica Camera AG, was designed by Oskar Barnack in 1913. This box camera made of Bakelite is the simplest kind of photographic camera with a screw-in lens. In designing the device, the assumption was that the camera would be a cheap, small, and simple to use device for budding photo enthusiasts. Work on the prototype was completed in August 1955. The Druh camera was built following the model of the German Pouva Start camera, whose design was inspired by the pre-War French Photax II Blindé art déco-styled camera. Millions of Poles began their adventure with photography using the Druh cameras. As many as 1,100,000 units were built. Manufacturer: Warszawskie Zakłady Foto-Optyczne,1960s Inv. No.: MIM254/VI-46 Model prepared on the basis of photogrammetric measurements Licence: CC BY-NC-SA Source: Objaverse 1.0 / Sketchfab
Supplementary material 2 from: Mertens JEJ, Van Roie M, Merckx J, Dekoninck W (2017) The use of low cost compact cameras with focus stacking functionality in entomological digitization projects. ZooKeys 712: 141-154. https://doi.org/10.3897/zookeys.712.20505
Figure S2 : Explanation note: Comparison of the narrow (C, F), normal (A, B, D, G) and wide (E, H) focal step size in two specimens of different 'depth' (A, C–E: Allochroma sp., 2 mm deep; B, E–H: Doryphora sp., 12 mm deep, measured from top of elytra to lowest tarsi). The narrow setting is marginally sharper in some areas; however, deeper parts of the specimen are not in focus. The wide setting produces artefacts around some of the edges, sometimes resulting in less sharp regions.
Supplementary material 1 from: Mertens JEJ, Van Roie M, Merckx J, Dekoninck W (2017) The use of low cost compact cameras with focus stacking functionality in entomological digitization projects. ZooKeys 712: 141-154. https://doi.org/10.3897/zookeys.712.20505
Figure S1 : Explanation note: Lightbox setup, the camera rests on top of the cut-off bucket, its lens protruding though the hole in the middle. The specimen is usually shielded from direct light by a free-standing cylinder of tracing paper (not depicted). The LED strips on the inside are powered through a 12V adapter.
GONet v2 camera calibration
<p>The following dataset contains images taken by the GONet All sky camera for the "GONet All sky camera calibration" LICA report.</p>
Data availability: Clustered and rotating designs as a strategy to obtain precise detection rates in camera trapping studies
<p>Manuscript data "Clustered and rotating designs as a strategy to obtain precise detection rates in camera trapping studies" published in Journal of Applied Ecology. R code to replicate the simulations can be found in the supplementary materials of the manuscript.</p>
WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models
<p>#############</p> <h1>WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models</h1> <p>#############</p> <p>Authors: Valentin Gabeff, Marc Russwurm, Devis Tuia & Alexander Mathis</p> <p>Affiliation: EPFL</p> <p>Date: January, 2024</p> <p>Link to the article: <a href="https://link.springer.com/article/10.1007/s11263-024-02026-6">https://link.springer.com/article/10.1007/s11263-024-02026-6</a></p> <p>--------------------------------</p> <p>WildCLIP is a fine-tuned CLIP model that allows to retrieve camera-trap events with natural language from the Snapshot Serengeti dataset. This project intends to demonstrate how vision-language models may assist the annotation process of camera-trap datasets.</p> <p>Here we provide the processed Snapshot Serengeti data used to train and evaluate WildCLIP, along with two versions of WildCLIP (model weights).</p> <p>Details on how to run these models can be found in the project <a href="https://github.com/amathislab/wildclip">github repository</a>.</p> <h2>Provided data (images and attribute annotations): </h2> <p>The data consists of 380 x 380 image crops corresponding to the MegaDetector output of Snapshot Serengeti with a confidence threshold above 0.7. We considered only camera trap images containing single individuals.</p> <p>A description of the original data can be found on LILA <a href="https://lila.science/datasets/snapshot-serengeti">here</a>, released under the <a href="https://cdla.dev/permissive-1-0/" rel="nofollow">Community Data License Agreement (permissive variant)</a>.</p> <p>We warmly thank the authors of LILA for making the MegaDetector outputs publicly available, as well as for structuring the dataset and facilitating its access.</p> <h2>Adapted CLIP model (model weights): </h2> <p>WildCLIP models provided:</p> <ul> <li><strong>[New] WildCLIP_vitb16_t1.pth: </strong>CLIP model with the ViT-B/16 visual backbone trained on data with captions following template 1. Trained on both base and novel vocabulary (see paper for details).</li> <li><strong>[New] WildCLIP_vitb16_t1_lwf.pth: </strong>CLIP model with the ViT-B/16 visual backbone trained on data with captions following template 1, and with the additional VR-LwF loss. Trained on both base and novel vocabulary (see paper for details).</li> <li><strong>WildCLIP_vitb16_t1_base.pth:</strong> CLIP model with the ViT-B/16 visual backbone trained on data with captions following template 1. Model used for evaluation and trained on base vocabulary only. (previously named <em>WildCLIP_vitb16_t1.pth</em>)</li> <li><strong>WildCLIP_vitb16_t1t7_lwf_base.pth</strong>: CLIP model with the ViT-B/16 visual backbone trained on data with captions following templates 1 to 7, and with the additional VR-LwF loss. Model used for evaluation and trained on base vocabulary only. (previously named <em>WildCLIP_vitb16_t1t7_lwf.pth</em>)</li> </ul> <p>We also provide the CSV files containing the train / val / test splits. The train / test splits follow camera split from LILA (https://lila.science/datasets/snapshot-serengeti). The validation split is custom, and also at the camera level.</p> <ul> <li><strong>train_dataset_crops_single_animal_template_captions_T1T7_ID.csv</strong>: Train set with captions from templates 1 through 7 (column "all captions") or template 1 only (column "template 1")</li> <li><strong>val_dataset_crops_single_animal_template_captions_T1T7_ID.csv</strong>: Validation set with captions from templates 1 through 7 (column "all captions") or template 1 only (column "template 1")</li> <li><strong>test_dataset_crops_single_animal_template_captions_T1T8T10.csv</strong>: Test set with captions from templates 1, 8, 9 and 10 (columns "all captions")</li> </ul> <p>Details on how the models were trained can be found in the associated <a href="https://link.springer.com/article/10.1007/s11263-024-02026-6" target="_blank" rel="noopener">publication</a>.</p> <h2>References: </h2> <p>If you find our code, or weights, please cite:</p> <pre>@article{gabeff2024wildclip, title={WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models}, author={Gabeff, Valentin and Ru{\ss}wurm, Marc and Tuia, Devis and Mathis, Alexander}, journal={International Journal of Computer Vision}, pages={1--17}, year={2024}, publisher={Springer} }</pre> <p>If you use the adapted Snapshot Serengeti data please also cite their article:</p> <pre>@article{swanson2015snapshot, title={Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna}, author={Swanson, Alexandra and Kosmala, Margaret and Lintott, Chris and Simpson, Robert and Smith, Arfon and Packer, Craig}, journal={Scientific data}, volume={2}, number={1}, pages={1--14}, year={2015}, publisher={Nature Publishing Group} }</pre>
Impact crater identification results for Chang'e-4 satellite image area and landing camera image area
<p>Here are the results of impact crater identification from Chang'e-4 satellite image data and Chang'e-4 landing camera image data.</p> <p>There are 99 impact craters identified in the landing camera, with diameters ranging from 1.55m to 50.87m.There are 947 impact craters identified in the satellite images, with diameters ranging from 48.37m to 2,222.34m in diameter.</p> <p>All impact craters are categorised into five degradation classes (A, AB, B, BC, C).</p> <p>Landing camera image source: <a href="https://doi.org/10.5281/zenodo.3600427" target="_blank" rel="noopener noreferrer">https://doi.org/10.5281/zenodo.3600427</a><br>Satellite image source: <span>https://darts.isas.jaxa.jp/planet/pdap/selene/index.html.en</span></p> <p> </p>
Camera trap data of mammals from Baluran National Park
<p>Dholes (<em>Cuon alpinus</em>) are endangered large carnivores found in scattered populations in Asia. One of the main threats to dholes is the decreasing prey availability throughout their distribution range. In the present study we used camera trap data collected over six years to investigate the temporal activity patterns of dholes and their putative prey species in Baluran National Park in Java, Indonesia. We also explored the overlap in activity between dholes and the park's other remaining large carnivore the Javan leopard (<em>Panthera pardus melas</em>), as well as humans. Furthermore, we investigated potential differences in activity patterns between dholes in packs and dholes roaming in pairs or alone. We found a high temporal overlap between dholes and their wild ungulate prey species (ranging from D=0.66–0.90), with the lowest overlap observed between dholes and bantengs (<em>Bos javanicus</em>) (D=0.66), and the highest between dholes and muntjacs (<em>Muntiacus muntjak</em>) (D=0.90). A very low overlap was found between dholes and domestic cattle (<em>Bos indicus</em>) (D=0.27) whereas a moderately high overlap was found between dholes and leopards (D=0.70) and dholes and humans (D=0.62). We found a significant difference in activity patterns between dholes in packs and dholes roaming alone or in pairs (D=0.78, p=0.01). Single/pairs of dholes were more active both during the day and at night, whereas packs were predominantly active around sunrise and sunset. The high overlap with humans potentially has a negative effect on dhole activity, particularly for dispersing individuals, and the low overlap with domestic species questions the extent to which dholes are considered to predate on them.</p>
MC-GTA: A Synthetic Benchmark for Multi-Camera Vehicle Tracking
<p><strong>Dataset</strong></p> <p>The MC-GTA dataset is designed for multi-camera vehicle tracking (MCVT) in urban environments, crucial for city-scale traffic analysis, management, and security applications. Traditional MCVT systems face challenges due to the scarcity of annotated data necessary for training and testing deep learning-based computer vision models. To address this, the MC-GTA dataset offers a synthetic collection of urban scene images captured from the virtual environment of the Grand Theft Auto 5 (GTA) video game. This dataset features recordings from multiple cameras placed at various crossroads, with automatically generated annotations including bounding boxes and unique vehicle IDs consistent across different video sources. The dataset aims to provide a valuable benchmark for MCVT tasks, demonstrating its utility through performance evaluation with a state-of-the-art MCVT approach. Additionally, the dataset and tools for creating custom scenarios are publicly accessible at <a href="https://github.com/GaetanoV10/GT5-Vehicle-BB" target="_new" rel="noreferrer">https://github.com/GaetanoV10/GT5-Vehicle-BB</a>.<br><br></p> <p><strong>Citing the MC-GTA</strong></p> <p>The MC-GTA is released under a Creative Commons Attribution license, so please cite the MC-GTA if it is used in your work in any form.<br>Published academic papers should use the academic paper citation for our MC-GTA paper</p> <blockquote> <pre>@inproceedings{ciampi2023mc, title={Mc-gta: A synthetic benchmark for multi-camera vehicle tracking}, author={Ciampi, Luca and Messina, Nicola and Valenti, Gaetano Emanuele and Amato, Giuseppe and Falchi, Fabrizio and Gennaro, Claudio}, booktitle={International Conference on Image Analysis and Processing}, pages={316--327}, year={2023}, organization={Springer} }</pre> </blockquote> <p>Personal works, such as machine learning projects/blog posts, should provide a URL to the MC-GTA<strong> </strong>Zenodo page (<a href="https://doi.org/10.5281/zenodo.5996890">https://doi.org/10.5281/zenodo.5996890</a>), though a reference to our MC-GTA paper would also be appreciated.</p> <p><strong>Contact Information</strong></p> <p>If you would like further information about the MC-GTA<strong> </strong>or if you experience any issues downloading files, please contact us at luca.ciampi[at]isti.cnr.it</p> <p><strong>Acknowledgements</strong></p> <p>Supported by: MOST - Sustainable Mobility National Research Center, funded by the European Union Next-GenerationEU (Piano Nazionale di Ripresa E Resilienza (PNRR) - Missione 4 Componente 2, Investimento 1.4 - D.D. 1033 17/06/2022, CN00000023); AI4Media – A European Excellence Centre for Media, Society, and Democracy (EC, H2020 No. 951911); SUN – Social and hUman ceNtered XR (EC, Horizon Europe No. 101092612).</p>
Raw EEG Data Publication "Embodying the camera"
<p>Raw data files of 16 subjects recorded during experiment as described in article: Heimann, K., Uithol, S., Calbi, M., Umiltà, M.A., Guerra, M., Fingerhut, J., Gallese, V. (submitted to PLOSONE) <strong>Embodying the camera: an EEG study on the effect of camera movements on film spectators´ sensory-motor cortex activation". </strong></p> <p>event trigger value for start of videos = stim, indices for still, zoom and steady condition in 9.xls file (1=still, 2=zoom, 3=steady)</p> <p>event trigger value for slide announcing action execution =ceck (response = resp)</p> <p>Data of further analysis steps available at request to katrinheimann@cas.au.dk</p> <p> </p> <p> </p>
Super-resolution in LWIR using 6 cameras
<p>Example of super-resolution in LWIR using 6 cameras, showing one of the original videos at the top, and the super-resolved video in the bottom, where improvement in resolution and SNR is observed.</p>
Supplementary material 5 from: Ribeiro-Silva L, Perrella DF, Biagolini-Jr CH, Zima PVQ, Piratelli AJ, Schlindwein MN, Galetti-Jr PM, Francisco MR (2018) Use of camera traps for detecting nest predation of birds in the Atlantic Forest of Brazil. Zoologia 35: 1-8. https://doi.org/10.3897/zoologia.35.e14678
Video of a nest of the Gray-hooded Flycatcher, Mionectes rufiventris, containing two nestlings, being depredated by Three-striped Short-tailed Opossum, Monodelphis americana. Note that the small mammal has climbed the closed nest, found its entrance, and has invaded the interior of the nest. :
Supplementary material 4 from: Ribeiro-Silva L, Perrella DF, Biagolini-Jr CH, Zima PVQ, Piratelli AJ, Schlindwein MN, Galetti-Jr PM, Francisco MR (2018) Use of camera traps for detecting nest predation of birds in the Atlantic Forest of Brazil. Zoologia 35: 1-8. https://doi.org/10.3897/zoologia.35.e14678
Video of an Ocelot, Leopardus pardalis, depredating a nest of Gray-hooded Flycatcher, Mionectes rufiventris. The video shows the moment in which a nestling try to leave the nest and is captured in the air by the Ocelot. :
Supplementary material 3 from: Ribeiro-Silva L, Perrella DF, Biagolini-Jr CH, Zima PVQ, Piratelli AJ, Schlindwein MN, Galetti-Jr PM, Francisco MR (2018) Use of camera traps for detecting nest predation of birds in the Atlantic Forest of Brazil. Zoologia 35: 1-8. https://doi.org/10.3897/zoologia.35.e14678
Video of a nest of the Blue Manakin, Chiroxiphia caudata, being depredated by The Red-breasted Toucan, Ramphastos dicolorus. The video shows a young in late nestling stage being removed from the nest and being carried away by the toucan. :
Supplementary material 2 from: Ribeiro-Silva L, Perrella DF, Biagolini-Jr CH, Zima PVQ, Piratelli AJ, Schlindwein MN, Galetti-Jr PM, Francisco MR (2018) Use of camera traps for detecting nest predation of birds in the Atlantic Forest of Brazil. Zoologia 35: 1-8. https://doi.org/10.3897/zoologia.35.e14678
Video of the Red-breasted Toucan, Ramphastos dicolorus, consuming an egg of the Ruddy Quail-dove, Geotrygon montana. Seven days later, a second egg present in this nest was also consumed by a Red-breasted Toucan. :
Supplementary material 1 from: Ribeiro-Silva L, Perrella DF, Biagolini-Jr CH, Zima PVQ, Piratelli AJ, Schlindwein MN, Galetti-Jr PM, Francisco MR (2018) Use of camera traps for detecting nest predation of birds in the Atlantic Forest of Brazil. Zoologia 35: 1-8. https://doi.org/10.3897/zoologia.35.e14678
Video of the Collared Forest-falcon, Micrastur semitorquatus, depredating a nest of White-necked Thrush, Turdus albicollis, containing three eggs. In this cloudy day, the infrared LEDs were activated. :
Data of Sedimentation process of ashfall during a Vulcanian eruption as revealed by high-temporal-resolution grain size analysis and high-speed camera imaging
<p>Data of ash falling rate, grain size analysis of ash deposit, and high-speed camera imaging of airborne ash particles used in "Sedimentation process of ashfall during a Vulcanian eruption as revealed by high-temporal-resolution grain size analysis and high-speed camera imaging". </p>
Data for: Scalable Flood Level Trend Monitoring with Surveillance Cameras using a Deep Convolutional Neural Network [Version 2]
<p>This package provides material that can be openly published for the paper "Scalable Flood Level Trend Monitoring with Surveillance Cameras using a Deep Convolutional Neural Network". It consists in the code used to generate results and figures as well as the weights of the deep convolutional neural networks trained to segment water in the surveillance camera images.</p>
FIGURE 3A–C. Camera lucida drawings, D. cambodiensis, A in Neotype designation and generic re-definition of the monotypic genus Dialeurotrachelus Takahashi (Hemiptera: Aleyrodidae), and first record of its occurrence in the Andaman and Nicobar Islands, India
FIGURE 3A–C. Camera lucida drawings, D. cambodiensis, A, puparium; B, thoracic tracheal pore; C, posterior abdominal area.
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.