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2,139 results for “recognition”
Videos For: One-shot recognition of any material anywhere using contrastive learning with physics-based renderin
<p>Demonstration Video:</p> <p>Image Recognition of Materials States And Types From Single Example Identifying the state of the material in the main video by matching it to a few reference images (top). Each image contains a specific material state or type. The best match image is marked green. Based on the net for identifying similarity between any material state and type. The code for the net and the trained model used for this is available at: https://github.com/sagieppel/Contrastive-learning-for-one-shot-materials-and-textures-similarity-recognition-from-images Paper : One-shot recognition of any material anywhere using contrastive learning with physics-based rendering. https://arxiv.org/abs/2212.00648</p>
Smartphone and smartwatch inertial measurements from heterogeneous subjects for human activity recognition.
<p>This repository contains the dataset and contents described in the <i><strong>"Dataset of inertial measurements of smartphones and smartwatches for human activity recognition"</strong></i> data article.</p><blockquote><p>Matey-Sanz, M., Casteleyn, S., & Granell, C. (2023). Dataset of inertial measurements of smartphones and smartwatches for human activity recognition. <i>Data in Brief</i>, 109809.</p></blockquote>
Towards a general open dataset and model for late medieval Castilian text recognition (HTR/OCR). Datasets and scripts
<p>This repository contains the dataset of the article "Towards a general open dataset and models for late medieval Castilian writing (HTR/OCR)" submitted to the Journal of Data Mining and Digital Humanities (JDMDH). I refer to the paper (<a href="https://doi.org/10.5281/zenodo.7387376">https://doi.org/10.5281/zenodo.7387376</a>) for the description of the corpus and the models.</p><p><strong>The dataset is in version V2: it contains the allographetic AND graphematic transcriptions (files `*.normalized.xml`) and models.</strong></p><p><i>Caveat</i>: the allographetic transcriptions and models only are described in the data paper mentionned above. The graphematic transcriptions are produced using a Chocomuffin conversion table (see `corpus/conversion_table.csv`) to reduce each allograph to its corresponding grapheme. The abbreviations are not expanded.</p><p>Please cite the following paper if you use this dataset or the models:</p><p>@article{gille_levenson_2023_towards,<br> author = {Gille Levenson, Matthias},<br> date = {2023},<br> journaltitle = {Journal of Data Mining and Digital Humanities},<br> doi = {<a href="https://doi.org/10.46298/jdmdh.10416">10.46298/jdmdh.10416</a>},<br> editor = {Pinche, Ariane and Stokes, Peter},<br> issuetitle = {Special Issue: Historical documents and automatic text recognition},<br> title = {Towards a general open dataset and models for late medieval Castilian text recognition<br>(HTR/OCR)},</p><p>GILLE LEVENSON , Matthias, « Towards a general open dataset and models for late medieval Castilian<br>text recognition (HTR/OCR) », <i>Journal of Data Mining and Digital Humanities</i> (2023) : Special<br>Issue : Historical documents and automatic text recognition, eds. Ariane PINCHE and Peter<br>STOKES, DOI : <a href="https://doi.org/10.46298/jdmdh.10416">10.46298/jdmdh.10416</a>.</p><p>The image of the manuscript M (Esc_M) has not yet been uploaded, pending permission from the library that keeps the manuscript.</p><p>All images are kept in a directory named after the place where the manuscript is kept, and the sigla of the witness for the in-domain dataset.</p><p> </p><p>The global licence for the dataset (except for images) is CC-BY-NC-SA.</p><p>All manuscripts reproductions are published with the authorization of the libraries.</p><p><strong>©Biblioteca General Histórica de Salamanca</strong></p><p>Universidad de Salamanca (España), Biblioteca General Histórica, Ms. 2709 (L)</p><p>Universidad de Salamanca (España), Biblioteca General Histórica, Ms. 2097 (J)</p><p>Universidad de Salamanca (España), Biblioteca General Histórica, Ms. 2673</p><p>Universidad de Salamanca (España), Biblioteca General Histórica, Ms. 2011</p><p>Universidad de Salamanca (España), Biblioteca General Histórica, Ms. 2654</p><p>Universidad de Salamanca (España), Biblioteca General Histórica, Ms. 2086</p><p><strong>©Museo Lázaro Galdiano. Madrid</strong></p><p>Inv. 15304, Fundación Lázaro Galdiano (A)</p><p><strong>©Universidad de Valladolid</strong></p><p>Ms. 251, Biblioteca Santa Cruz (S)</p><p><strong>©Real Biblioteca del Escorial</strong></p><p>Ms. K.I.5, Biblioteca del Real Monasterio del Escorial (Q)</p><p>Ms. h.I.8, Biblioteca del Real Monasterio del Escorial (M): to be published</p><p>Ms. Z-I-12</p><p>Ms.Z-III-9</p><p>Ms. X-III-4</p><p>Ms. h-III-9</p><p>Ms. b-IV-15</p><p>Ms. b-II-11</p><p>Ms. a-II-17</p><p>Ms. T-III-5</p><p><strong>©Rosenbach Foundation</strong></p><p>Ms. 482/2 (U)</p><p><strong>© Gallica.bnf.fr</strong></p><p>Espagnol 12</p><p>Espagnol 36</p><p>Espagnol 218</p><p><strong>© Bodleian Library</strong></p><p>Ms. Span. d. 1</p><p>Ms. Span. d. 2/1</p><p><strong>© Biblioteca Real, Madrid</strong></p><p>Ms. II/215 (G)</p><p><strong>© Biblioteca Nacional de España</strong></p><p>Mss/4183</p><p>Inc/901 (Z)</p><p><strong>© Biblioteca Universitaria, Sevilla</strong></p><p>Ms. 332/131 (R)</p><p> </p><p>Edit: add result files</p>
ICDAR 2023 CROHME: Competition on Recognition of Handwritten Mathematical Expressions
<p>Here is the datasets collected for the Competitionon Recognition of Online Handwritten Mathematical Expressions in competition session of ICDAR 2023. <br> 3 tasks are proposed with different modalities, there are on-line, off-line and bi-modal. <br> For on-line task, we provide .inkml file (contain trace information, mathML and LaTeX string), and also symbol level label graph (SymLG) as ground truth. Except the new data and previous CROHME data, we also provide huge amount of artificial on-line data in the train set. <br> For off-line task, the .png images (scanned from paper or rendering from inkml) and symbol level label graph (SymLG) are provided. Except the new data and previous CROHME data, we use off-line images from OffHME to increase the size of train set. <br> For bi-modal task, both .inkml file and ,png images are provided as 2 channels input, and SymLG as ground truth. </p> <p>All the 3 tasks inherited the data collected from the previous 6 CROHME, and also the new collection 2023 in 3 sites, Nantes (France), Luleå (Sweden) and Tokyo (Japan).</p>
Community Engagement for Early Recognition and Immediate Action in Stroke
ClinicalTrials.gov study NCT02301299. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Recognition of Second Twin. Second Training vs Naive Performer. PROMPT Mannequin
ClinicalTrials.gov study NCT06127706. IPD Sharing: NO. Countries: 1. Publications: 3.
Development and Validation of Delirium Recognition Using Computer Vision in Neuro-critical Patients
ClinicalTrials.gov study NCT07136207. IPD Sharing: NO. Countries: 1. Publications: 10.
Remediation of Auditory Recognition in Schizophrenia With tDCS
ClinicalTrials.gov study NCT02869334. IPD Sharing: NO. Countries: 1. Publications: 1.
A Survey of Factors Associated With the Successful Recognition of Agonal Breathing and Cardiac Arrest.
ClinicalTrials.gov study NCT00848588. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Diagnostic Performance of a Convolutional Neural Network for Diminutive Colorectal Polyp Recognition
ClinicalTrials.gov study NCT03822390. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Electronic Tools to Increase Recognition and Improve Primary Care Management for Hypertension in Chronic Kidney Disease
ClinicalTrials.gov study NCT03679247. IPD Sharing: NO. Countries: 1. Publications: 5.
Safety and Efficacy of Piromelatine in Mild Alzheimer's Disease Patients (ReCOGNITION)
ClinicalTrials.gov study NCT02615002. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Pattern Recognition and Anomaly Detection in Fetal Morphology Using Deep Learning and Statistical Learning
ClinicalTrials.gov study NCT05738954. IPD Sharing: YES. Countries: 1. Publications: 4.
Induction and Recognition of Emotions
ClinicalTrials.gov study NCT04353947. IPD Sharing: NO. Countries: 1. Publications: 6.
Data from: Mechanism of expanded DNA recognition in xCas9
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Data from: Recognition of endophytic Trichoderma species by leaf-cutting ants and their potential in a Trojan-horse management strategy
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Vocal recognition suggests premating isolation between lineages of a lekking hummingbird
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Data from: Predicting and measuring decision rules for social recognition in a Neotropical frog
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Raw data for: No reproductive benefits of dear enemy recognition in a territorial songbird
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Data from: Female mate preferences on high dimensional shape variation for male species recognition traits
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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.