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1,832 results for “Cameras”
Camera Spectral Sensitivity Database - Jiang et al. (2013)
<p><strong>Source URL</strong>: <a href="http://www.gujinwei.org/research/camspec/db.html">http://www.gujinwei.org/research/camspec/db.html</a><br> <strong>Source DOI</strong>: 10.1109/WACV.2013.6475015</p> <p>Camera spectral sensitivity functions relate scene radiance with captured RGB triplets. They are important for many computer vision tasks that use color information, such as multispectral imaging, and color constancy.</p> <p>We create a database of 28 cameras covering a variety of types. The database contains the spectral sensitivity functions for 28 cameras, including professional DSLRs, point-and-shoot, industrial and mobile phone camera. We use a spectrometer PR655 from Photo Research Inc., a light source and monochrometer combined with an integrating sphere to do the measurement. Each measurement starts from wavelength 400nm to 720nm in an interval of 10nm. Measured Sensitivities are normalized to 1 for RGB channels seperately. The database is in the form of a text file. Each entry starts with camera name and follows by measured spectral sensitivities in red, green and blue channel.</p>
Evolution of IT system and TEM camera performance
<p>This Excel file collates throughput specifications of transmission electron microscopy (TEM) cameras, mass storage, network and memory between 1996 and 2018. The absolute and relative development of the throughput is analyzed in charts.</p>
Fig. 2 in Camera Trapping The Indochinese Tiger, Panthera Tigris Corbetti, In A Secondary Forest In Peninsular Malaysia
Fig. 2. Cumulative number of individual tiger captured per month around FELDA Jerangau Barat, Terengganu between April 2000 to September 2000.
Fig. 3 in Camera Trapping The Indochinese Tiger, Panthera Tigris Corbetti, In A Secondary Forest In Peninsular Malaysia
Fig. 3. Identification of tiger individuals from infra-red sensor camera traps. Example of individual identification of tiger cubs (a, b) and adults (c, d) based on stripe patterns.
Fig. 2 in Camera-Trapping Survey Of Mammals In And Around Imbak Canyon Conservation Area In Sabah, Malaysian Borneo
Fig. 2. The observed species accumulation curve (-o-) and 95% CIs (---) for mammalian species in and around Imbak Canyon Conservation Area. The curve was constructed using abundancebased rarefaction approach (i.e., by using the number of independent photographs captured) with 100 randomisation runs in EstimateS (Colwell, 2009).
Fig. 3. Activity patterns for 14 in Camera-Trapping Survey Of Mammals In And Around Imbak Canyon Conservation Area In Sabah, Malaysian Borneo
Fig. 3. Activity patterns for 14 mammal species (with n ≥ 8) photocaptured in and around Imbak Canyon Conservation Area in central Sabah, Malaysian Borneo. Dotted bar indicates percent frequency of independent photographs taken during the day time (0600–1800 hours); Black bar indicates percent frequency of independent photographs taken during night time (1800–0600 hours). Species are listed in order of decreasing frequency of diurnal activity. Numbers in parentheses indicate sample size.
Fig. 1 in Camera-Trapping Survey Of Mammals In And Around Imbak Canyon Conservation Area In Sabah, Malaysian Borneo
Fig. 1. Imbak Canyon Conservation Area (ICCA) in central Sabah, northern part of Malaysian Borneo. Circles show the localities of 13 plots (P1–P13) where camera traps were placed (+). Each plot is approximately 3.5 km in radius.
Figs 2-5 in Accessing camera trap survey feasibility for estimating Blastocerus dichotomus (Cetartiodactyla, Cervidae) demographic parameters
Figs 2-5. Individual discrimination of marsh deer males (Blastocerus dichotomus Illiger, 1815) using antler morphology: a spike-antlered male (2) and two different branched antlers (3 and 4). The arrows indicate the different horn tips. Marsh deer female accompanied by a fawn (5).
Fig. 1 in Accessing camera trap survey feasibility for estimating Blastocerus dichotomus (Cetartiodactyla, Cervidae) demographic parameters
Fig. 1. Map outlining the aerial and camera trap surveys conducted at the Jataí Ecological Station, state of SÃo Paulo, Brazil in order to obtain marsh deer demographic parameters.
Meteor Camera Images - Sony IMX29
<p>Meteor Camera Images taken from a stationary Sony IMX29 camera.<br>I collected this data to make a Meteor Camera. <br><br><br>Feel free to use, but please make sure I get credited.<br>I am using these cameras both with my own trained model and also with The Global Meteor Network's Software.<br><br></p>
ENDGAME - Laboratory Experiment 2024-03-19 Exp. 003 - High Speed Camera data
<p>Preliminary 2D Shock-tube experiments in combination with high speed Schlieren shadow photography. </p> <p>We developed a 2D shock-tube setup using 2 glass sheets (1 cm width) separated by 2 lateral bars (gap between glass sheets 1.3 cm). We injected compressed air into the 2D setup at different overpressures (up to 8 bar). The high-pressure reservoir is connected with the 2D apparatus through a diaphragm pulse valve which allows a fast release of pressurized gas into the system. Images were collected at a frame rate of 30000 fps. The field of view of the images show the jet flow dynamics in the upper part of the 2 glass sheets and in the atmosphere.</p>
The Longyearbyen all-sky camera full resolution image data (movie and four ASC data plots) used in the paper entitled "Auroral Morphological Changes to the Formation of Auroral Spiral during the Late Substorm Recovery Phase: Polar UVI and Ground All-Sky Camera Observations"
<p>The uploaded movie is an animation of the Longyearbyen all-sky camera (ASC) full resolution image data from 20:00 UT to 22:00 UT, which is including all ASC snapshots used in Figure 2. This movie file is the same as Movie S1. </p> <p>The uploaded four png files are the Longyearbyen all-sky camera (ASC) full resolution image snapshots, which were used in Figure S3.</p> <p>The numerical ASCII data to make the four ASC full resolution image snapshots are also uploaded; the count number of data detected by the ASC and associated latitude and longitude information in geographical coordinates of the ASC field of view.</p>
Datasets for time-lapse camera monitoring of insects and their floral environments
<p>Contains the dataset for training and validation of models to estimate flower cover and identify taxa of arthropods in time-lapse camera recordings described in the paper:</p> <p>Kim Bjerge, Henrik Karstoft, Hjalte M. R. Mann, Toke T. Høye, A deep learning pipeline for time-lapse camera monitoring of insects and their floral environments, 2024, bioRxiv, <a href="https://doi.org/10.1101/2024.04.12.589205" rel="noopener">https://doi.org/10.1101/2024.04.12.589205</a></p> <p>The zip files contain the needed files and directory structure to train the models in Python code published at: <a href="https://github.com/kimbjerge/insectsFlowers">https://github.com/kimbjerge/insectsFlowers</a></p> <p>Content of zip files:<br>===============</p> <p>insects.zip: Contains images and labels in YOLO format: <a href="https://github.com/ultralytics/yolov5/issues/2293">https://github.com/ultralytics/yolov5/issues/2293</a></p> <p>trainI21m contains the images and labels to train the insect detector with YOLOv5. Contains only the motion-informed enhanced images (MIE).<br>testI21m contains the images and labels to test the insect detector trained with YOLOv5. Contains only the motion-informed enhanced images (MIE).</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Flowers.zip: contains the images of plants and flowers with black and white masks to train the DeepLabv3 flower semantic segmentation model.</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>NI2-19cls.zip: contains images for training and validation of the arthropod classifiers </p> <p>Image crops of arthropods are organized in 19 subdirectories one for each class.</p> <p>A1-Coccinellidae<br>B2-Coleoptera<br>C3-Background<br>D4-Bombus<br>E5-Syrphidae<br>F6-Lepidoptera<br>G7-Aranaeae<br>H8-Formidicidae<br>I9-Diptera<br>J10-Hemiptera<br>K11-Isopoda<br>L12-Uspecificerede<br>N13-Hymenoptera<br>O14-Orthoptera<br>P15-Rhagonycha_fulva<br>Q16-Satyrinae<br>R17-Aglais_urticea<br>S18-Odonata<br>T19-Apis_mellifera</p>
Exploring multi-camera views from user-generated sports videos
<div> <p><strong>The proliferation of mobile devices</strong> with video recording capabilities has revolutionized the creation, sharing, and consumption of audiovisual content, turning user-generated video (UGV) platforms into major data sources. </p> <div> <div> <p><strong>Despite this growth</strong>, there is a notable gap in the availability of public datasets featuring multi-angle recordings of sports events captured by various mobile cameras. This led to the creation of the <strong>MUVY Dataset</strong>, with the name stemming from <strong>Multiview User-generated Videos from YouTube.</strong></p> <div> <div> <p>The dataset offers a diverse collection of sports videos from multiple perspectives, without restrictions on video size. In its first version, it covers sports like, <strong>American football, artistic gymnastics, athletics, basketball, tennis, and cricket.</strong></p> <div> <div> <div> <p>The dataset addresses common challenges in user-generated videos, such as shaking, occlusions, blurring, and abrupt movements. Each video is accompanied by metadata including camera identification, YouTube URLs, extracted frames, and object annotations.</p> </div> </div> </div> </div> </div> </div> </div> </div>
Photos from WIDER UPTAKE case study site in the Czech Republic (Camera 4)
<p>Hourly photos from camera 4 at the WIDER UPTAKE H2020 project's case study site in the Czech Republic.</p>
Acoustic video cameras multi-species multi-cameras Training Dataset (TD) for Deep Learning applications
<p>This images dataset, called also TD (Training Dataset), is designed to train/test/validate deep learning models to identify fish species in sonar camers video flux. It includes data from two different type of cameras (ARIS and DIDSON), two sites (Touques and Selune rivers in Normandy, France), 6 different fishes classes (Atlantic Salmon, European Eel, Sea Lamprey, Allis Shad, European Catfish and generic unidentified fish). This dataset, formatted in the yolo-darknet format as explained in <a href="https://github.com/AlexeyAB/darknet">https://github.com/AlexeyAB/darknet</a>, includes also a large number of images without any fish passage, in order to test the effect of negative data on the trainings.</p>
Fig. 2 in A Camera Trapping Inventory For Mammals In A Mixed Use Planted Forest In Sarawak
Fig. 2. Some of the photographs taken by camera during the study period showing how animals respond to a lure. A, Helarctos malayanus, Malayan Sun Bear; B, Sus barbatus, Bearded Pig; C, Viverra tangalunga, Malay Civet / Tangalung.
Fig. 1 in A Camera Trapping Inventory For Mammals In A Mixed Use Planted Forest In Sarawak
Fig. 1. Location of Planted Forest Zone within Bintulu Division in Sarawak, Malaysia. Planted Forest Zone is indicated in dark striped areas in center of map.
Fig. 2 in The first recorded activity pattern for the Sunda stink-badger Mydaus javanensis (Mammalia: Carnivora: Mephitidae) using camera traps
Fig. 2. Daily activity patterns of Sunda stink-badger within Lots 5 and 7 of the Lower Kinabatangan Wildlife Sanctuary, Sabah, Borneo. The grey areas represent an extension of the activity pattern to depict its circular nature, and 'carpet' marks along the x-axis represent individual photographic events. Vertical dashed red lines indicate either the end or beginning of the diurnal phase of the diel (0700–1659 hours). Vertical blue lines indicate either the end or beginning of the nocturnal phase of the diel (1900–0459 hours). Regions between red and blue lines represent the crepuscular regions of the diel (0500–0659 hours and 1700–1859 hours).
Fig. A3 in The first recorded activity pattern for the Sunda stink-badger Mydaus javanensis (Mammalia: Carnivora: Mephitidae) using camera traps
Fig. A3. Co-occurrence of Sunda stink-badger Mydaus javanensis and Malay civet Viverra tangalunga photo-captured in the Lower Kinabatangan Wildlife Sanctuary, Sabah, Malaysian Borneo on 13 June 2013.
ScienceDex guides
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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.