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27 results for “video processing”
Videos of the processed microscope images and time series of the petrophysical parameters from image processing and geochemical simulation and of the measured induced polarisation [Video][Dataset]
<p>Supporting Information for the manuscript <em>Microfluidics and spectral induced polarization for direct observation and petrophysical modeling of calcite dissolution</em> published in Geophysical Research Letters</p> <ul> <li><strong>Data Set S1.</strong> Porosity, water saturation, and calcite sample perimeter from image<br>processing.</li> <li><strong>Data Set S2.</strong> Porosity, water conductivity, and pH from geochemical simulation.</li> <li><strong>Data Set S3.</strong> Real and imaginary components of the complex electrical conductivity at<br>2.5 Hz and CEC from petrophysical modeling.</li> <li><strong>Movie S1.</strong> Dissolution of the calcite sample with the detected contour superimposed in<br>white on the grayscale images. Time, length scale, and flow direction are indicated. In<br>case of problems launching the file, we recommend using VLC Media Player software.</li> <li><strong>Movie S2.</strong> Segmented images of the CO2 bubbles produced by the calcite dissolution.<br>Time, length scale, and flow direction are indicated. In case of problems launching the<br>file, we recommend using VLC Media Player software.</li> </ul>
IN_CERT- The work process video
<p>IN_CERT is an initiative lead by the <a href="https://alella-cat.translate.goog/canmanye?_x_tr_sl=auto&_x_tr_tl=en&_x_tr_hl=en">Can Manyé Art Center</a> that arose in a context of pandemic and health crisis unprecedented in our contemporaneity, which aims to make visible the links between science, art and health, the transformative capacity of art and also its social and humanitarian dimensions for general public. The Germans Trias i Pujol Hospital (HUGTIP) and the Germans Trias i Pujol Research Institute (IGTP), through the <a href="https://www.smatb.eu/">SMA-TB project</a>, have participated in IN_CERT. <a href="https://www.smatb.eu/">SMA-TB</a> and <a href="https://www.mistral-hiv.eu/">MISTRAL</a> project coordinators were 2 of the 3 members of the stable group of scientists involved in the initiative. IN_CERT generated several outputs: debates, a book, this video and conferences. </p> <p>This video shows the work process over the course of a year, with regular meetings, made up of a stable group of artists, scientists and people linked to the art world. </p>
Collection of videos documenting the silk weaving process (Silk pilot, Mingei)
<p>Documentation videos of the Weaving process from the Silk pilot of the Mingei project.</p>
Data and code for article "Nature reserve customized method of photo and video camera traps materials processing using two-stage neural network approach"
<p><strong>DESCRIPTION</strong> 📓</p> <p>"data" folder directory contains the datasets for classification and detection. </p> <ol> <li>The detection dataset has <strong>YOLOv5 format</strong> and contains three classes <strong>[tigers, leopards, empty]</strong>. The class empty is about <strong>10%</strong> of the total data. The leopard and tiger classes contain <strong>3500</strong> images each. The entire amount of data for the detection task is <strong>7600</strong> images.</li> <li>The classification dataset contains two classes <strong>[tigers, leopards]</strong>. Images for classification are cropped images from the detection task using bounding boxes. Each class has <strong>3500</strong> images</li> </ol> <p> </p> <p>The "weights" folder contains pretrained models for classification and detection tasks. </p> <ul> <li>The detector weights were pre-trained on <strong>231k</strong> images from camera traps located throughout Russia.</li> <li>The classifier weights were pre-trained on <strong>416k</strong> images that were cropped with <strong>bounding boxes</strong> from photographs for the detection task. Some of the images for the classification task were taken from the <strong>Internet</strong>. The classifiers were trained for <strong>29 classes</strong>.</li> <li>You can also find folder <strong>tigers_vs_leopards</strong> in both the detection and classification directory, where there are weights that have been trained on a part of the camera trap images available at the link below.</li> </ul> <p><em>Classification weights</em></p> <ol> <li>EfficientNetv2-M</li> <li><strong>ResNeSt-101e</strong> (🚀 RECOMMENDED)</li> <li>ResNet-101d</li> <li>ReXnet-100</li> <li>SeResNet-152d</li> </ol> <p><em>Detection weights</em></p> <ol> <li>YOLOR-W6-1280</li> <li>YOLOX-X-640</li> <li>YOLOv5-X-640</li> <li>YOLOv5-X-1280</li> <li>YOLOv5-M6-1280</li> <li><strong>YOLOv5-L6-1280</strong> (🚀 RECOMMENDED)</li> </ol> <p>Read README.md file for more details</p>
Dataset for surface waves height prediction through the video and image processing
<p>Image-based study of surface waves is a long lasting topic in ocean science and remote sensing. We believe that modern computers and new programming techniques can make a break-through in this area.</p> <p> </p> <p>This dataset provides some video files of surface wind waves of two kinds. First is a video snapshot of a quite large area. Second one is a zoom-in video of a spar-buoy (a stick) located in this field. According to the zoom-in video we may see the actual height of the wave in this particular point. This should be treated as a reliable data and so it can be used to calibrate the brightness field. I.e. the users of this dataset are welcome to train their model to obtain the height of the wave out of its brightness on the zoom-out large-area videos.</p> <p> </p> <p>All video files are readable by a conventional software. Records were taken at mild wind conditions in a gulf (fjord or skerry) of the Ladoga Lake. See "readme.pdf" for the details</p>
FLAMINGo video: Extrusion Process by Constellium
<p>The extrusion process at Constellium's University Technology Center involves manufacturing Al-MMC specimens and parts. Widely recognized by car manufacturers for its cost-effectiveness, extrusion is a preferred method for crafting the Body in White (BIW) of a car. Following extrusion, the video showcases the investigation of quenching, a rapid cooling process that sets desired properties in aluminum components, ensuring the necessary mechanical and structural characteristics. This combination of extrusion and quenching is essential for shaping aluminum into required forms.</p>
Chimpanzee food processing video archive (Waibira community, Budongo Forest, Uganda)
<p>This is a video archive containing data (n = 474 videos) on food processing techniques and other behaviours used by the chimpanzees in the Waibira community, Budongo Forest, Uganda. Each video is labelled with data on date, location, individuals and behaviours present (including non-processing behaviours). </p> <p>Also included is the ethogram used to categorise processing behaviours as they are labelled in the archive, and 19 exemplar clips demonstrating some of these behaviours. This archive is intended to be used as a data ark for future interdisciplinary research. While the videos themselves cannot be made publicly accessible, those interested in collaborations may browse the available videos and contact the researchers to request access to specific videos. </p>
In-vivo video microscopy of the rupturing process of thin blood vessels in transparent fish during contact with a blunt indenter
<p>To clarify the mechanism of bruise injuries caused by blunt impact, in vivo microscopy was performed. A flat ended indenter made of transparent acrylic was loaded onto the lateral side of the tail region of anesthetized fish. The process of the rupture of thin blood vessels was recorded in two specimens.</p> <p>This data set includes two types of files; (1) mp4 files of the in vivo microscopy; (2) pdf files to explain the mp4 files.</p> <p><span>The mp4 files are the original data of “Fujikawa, T., Yamada, Y. In vivo video microscopy of the rupturing process of thin blood vessels to clarify the mechanism of bruising caused by blunt impact: an animal study. BioMed Eng OnLine 23, 94 (2024). https://doi.org/10.1186/s12938-024-01284-2.”</span></p>
Penetration and perforation process of 12.7mm FSP on plate captured by high-speed camera 2: Video (Test 2 of configuration 2).
<p>The penetration and the perforation process of configuration 2 is recorded using HSC 2. It is shown that at the moment of impact, the FSP causes a localized bulge at the rear side of the plate. A localized shearing of material, induced by the FSP front surface, is created in the contact zone. Immediately after, the FSP penetrates and a circular plug is punched through the plate, leaving a clean cut hole. Subsequently, the punched plug can be seen attached to the front surface of the projectile. After that, the punched plug is separated from the FSP.</p>
A data archive including processed spiking data, raw EMG datasets, and video data during locomotion behavior from six mice
Open the record for dataset details and reuse information.
Supplemental Material: Analysis of video games on the teaching-learning process in children from 3 to 4 years old
<p>Supplementary material of the results obtained in the paper Analysis of video games on the teaching-learning process in children from 3 to 4 years old.</p>
Porcelain process raw videos audio files
<p>Porcelain process raw videos audio files</p>
Audio processing and editing videos
<p>List of audio processing and editing videos</p> <p>Videos are in Spanish with test questions during reproduction</p>
A Gaussian process emulator for simulating ice sheet-climate interactions on a multi-million year timescale: CLISEMv1.0 (Video supplement)
<p>This video illustrates the ice sheet evolution during a 3 Myr period for three different emulators as described in the manuscript "A Gaussian process emulator for simulating ice sheet - climate interactions on a multi-million year timescale", submitted to Geoscientific Model Development. The ice sheet is forced by declining carbon dioxide concentrations from 980 to 720 ppmv and orbital parameter variations during the late Eocene (between 38 Ma and 35 Ma).</p>
Video Demonstration of a tool supporting iStar modeling process
<p>A video demonstrating a prototype tool for iStar modeling, additional material for the paper Understanding and satisfying the requirements for facilitating iStar modeling: a tool-supported modeling process.</p>
2023 IEEE SPS Video and Image Processing (VIP) Cup: Ophthalmic Biomarker Detection
<p>Ophthalmic clinical trials that study treatment efficacy of eye diseases are performed with a specific purpose and a set of procedures that are predetermined before trial initiation. Hence, they result in a controlled data collection process with gradual changes in the state of a diseased eye. In general, these data include 1D clinical measurements and 3D optical coherence tomography (OCT) imagery. Physicians interpret structural biomarkers for every patient using the 3D OCT images and clinical measurements to make personalized decisions for every patient.</p> <p>Two main challenges in medical image processing has been <em>generalization</em> and <em>personalization</em>.</p> <p>Generalization aims to develop algorithms that work well across diverse patients and scenarios, providing standardized and widely applicable solutions. Personalization, in contrast, tailors algorithms to individual patients based on their unique characteristics, optimizing diagnosis and treatment planning. Generalization offers broad applicability but may overlook individual variations. Personalization provides tailored solutions but requires patient-specific data. While deep learning has shown an affinity towards generalization, it is lacking in personalization.</p> <p>The presence and absence of biomarkers is a personalization challenge rather than a generalization challenge. The variation within OCT scans of patients between visits can be minimal while the difference in manifestation of the same disease across patients may be substantial. The domain difference between OCT scans can arise due to pathology manifestation across patients, clinical labels, and the visit along the treatment process when the scan is taken. Morphological, texture, statistical and fuzzy image processing techniques through adaptive thresholds and preprocessing may prove substantial to overcome these fine-grained challenges. This challenge provides the data and application to address personalization.</p>
2023 IEEE SPS Video and Image Processing (VIP) Cup: Ophthalmic Biomarker Detection Phase 2
<p>Ophthalmic clinical trials that study treatment efficacy of eye diseases are performed with a specific purpose and a set of procedures that are predetermined before trial initiation. Hence, they result in a controlled data collection process with gradual changes in the state of a diseased eye. In general, these data include 1D clinical measurements and 3D optical coherence tomography (OCT) imagery. Physicians interpret structural biomarkers for every patient using the 3D OCT images and clinical measurements to make personalized decisions for every patient.</p> <p>Two main challenges in medical image processing has been <em>generalization</em> and <em>personalization</em>.</p> <p>Generalization aims to develop algorithms that work well across diverse patients and scenarios, providing standardized and widely applicable solutions. Personalization, in contrast, tailors algorithms to individual patients based on their unique characteristics, optimizing diagnosis and treatment planning. Generalization offers broad applicability but may overlook individual variations. Personalization provides tailored solutions but requires patient-specific data. While deep learning has shown an affinity towards generalization, it is lacking in personalization.</p> <p>The presence and absence of biomarkers is a personalization challenge rather than a generalization challenge. The variation within OCT scans of patients between visits can be minimal while the difference in manifestation of the same disease across patients may be substantial. The domain difference between OCT scans can arise due to pathology manifestation across patients, clinical labels, and the visit along the treatment process when the scan is taken. Morphological, texture, statistical and fuzzy image processing techniques through adaptive thresholds and preprocessing may prove substantial to overcome these fine-grained challenges. This challenge provides the data and application to address personalization.</p> <p> </p> <p>These files constitute the second phase of the VIP CUP 2023 Challenge at ICIP 2023. This test set has a more general patient base than the first one and as such is a better indicator of the performance of models. This test set was created by taking a subset of the data from a publicly available OCT dataset and then asking our medical partners to provide fine-grained biomarker labels for the competition. We provide the citation for the source of these images below: </p> <p>Kermany D, Goldbaum M, Cai W et al. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell. 2018; 172(5):1122-1131. doi:10.1016/j.cell.2018.02.010.</p> <p> </p> <p>This zenodo repository contains the images and submission template file needed for the second phase of the competition.</p>
Single- Vs. Triple-Injection Techniques for Intertransverse Process Blocks in Video-Assisted Thoracoscopic Surgery: a Randomized Trial on Sensory Blockade
ClinicalTrials.gov study NCT06210958. IPD Sharing: NO. Countries: 1. Publications: 20.
Online Trial Examining Validity of the Shared Decision Making Process Survey With Video Vignettes
ClinicalTrials.gov study NCT04317274. IPD Sharing: YES. Countries: 1. Publications: 0.
Vision Video - Interaction Process of the Purchase of a Product by a Customer in a Webshop
<p>This vision video shows the interaction process between a customer and a webshop to purchase a product.</p> <p>The video was produced with the software tool <em>Mockup Recorder</em> for an experiment to evaluate its influence on requirements communication for shared understanding. The software tool <em>Mockup Recorder</em> is a prototypical implementation of the approach proposed by Oliver Karras, Caroline Unger-Windeler, Lennart Glauer and Kurt Schneider. Further information on the experiment can be found in the corresponding <a href="https://ieeexplore.ieee.org/abstract/document/8054839/">publication</a>.</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.