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2,139 results for “recognition”
Language Recognition for SSB modulated HF Radio Signals of Short Duration - Example Files
<p>These files are example files of the dataset used in our work "Language Recognition for SSB modulated HF Radio Signals of Short Duration". Two files are 10 second HF radio segments from Russian amateur radio communications. The other six files are a single recording from CommonLanguage (https://huggingface.co/datasets/common_language) and five modified versions of this file, created by applying the proposed HF radio simulation approach. Additional details can be found in the Paper.</p>
Burial mounds dataset associated with the paper Geomorphometric methods for tumuli recognition and extraction from high resolution LiDAR DEMs
<p>This dataset consist of the burial (tumuli) mounds delineation and the associated data produced for the article Geomorphometric methods for tumuli recognition and extraction from high resolution LiDAR DEMs, submitted to Sensors. The dataset work in conjunction with the script (http://doi.org/10.5281/zenodo.3628805) to make the work reproductible. The DEM is available only by request to mihai.niculita@uaic.ro.</p>
Dataset from "Collection of kinematic and kinetic data of young & adult, male & female subjects performing periodic and transient gait tasks for gait pattern recognition"
<p>Written by: Paolo Mistretta<br> Contact information: paolo.mistretta@phd.unipd.it<br> Date: 24/01/2020</p> <p><br> This document contains supplementary material for the article<br> “Collection of kinematic and kinetic data of young & adult, male & female subjects performing periodic and transient gait tasks for gait pattern recognition”<br> (Authors: Paolo Mistretta, Cecilia Marchesini, Andrea Volpini, Luca Tagliapietra, Tommaso Sciarra, Aldo Lazich, Salvatore Forte, Mauro De Matteis, Emanuele Menegatti and Nicola Petrone)<br> presented at the 13th conference of the International Sports Engineering Association, Tokyo, Japan, 22-25 June 2020.</p> <p><br> Data are contained in the file: “database_ISEA2020.mat”</p>
Benchmark for the evaluation of named entity recognition over ancient documents
<p>The dataset consists of a multilingual noisy corpora for named entity recognition (NER).<br> The noisy versions are simulated from the CoNLL-02 (Spanish and Dutch) and CoNLL-03 (English) NER corpora.<br> The original collections are re-OCRed and four types of noises at two different levels are added in order to simulate various OCR output.</p> <p>More precisely, we first extracted raw texts and converted them into images. These images have been contaminated by adding some common noises when using a scanner. We further extract OCRed data using tesseract open source<br> OCR engine v-3.04.01. Consequently to the image noise insertions, OCRed data contains degradations. Original and noisy texts are finally aligned.</p> <p>This archive contains three folders (one per language). The folders contain the degraded images, the noisy texts extracted by the OCR and their aligned version with clean data.</p> <p>These are the supplementary materials for the TPDL 2020 paper <a href="https://zenodo.org/record/4734376#.YJKAcKE6-Uk">Assessing and minimizing the impact of OCR quality on named entity recognition</a>. If you end up using whole or parts of this resource,<br> please cite this paper:</p> <pre><code>@InProceedings{10.1007/978-3-030-54956-5_7, author="Hamdi, Ahmed and Jean-Caurant, Axel and Sid{\`e}re, Nicolas and Coustaty, Micka{\"e}l and Doucet, Antoine", editor="Hall, Mark and Mer{\v{c}}un, Tanja and Risse, Thomas and Duchateau, Fabien", title="Assessing and Minimizing the Impact of OCR Quality on Named Entity Recognition", booktitle="Digital Libraries for Open Knowledge", year="2020", publisher="Springer International Publishing", address="Cham", pages="87--101", isbn="978-3-030-54956-5" }</code></pre> <p><strong>Acknowledgments</strong><br> This work has been supported by the European Union's Horizon 2020 research and innovation programme under grant 770299 [NewsEye](https://www.newseye.eu/).</p>
CURE-OR: Challenging Unreal and Real Environments for Object Recognition
<p>As one of the research directions at <a href="https://ghassanalregib.info/">OLIVES Lab @ Georgia Tech</a>, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (1.M images) object recognition dataset (<a href="https://github.com/olivesgatech/CURE-OR">CURE-OR</a>) which is among the most comprehensive datasets with controlled synthetic challenging conditions. In <a href="https://github.com/olivesgatech/CURE-OR">CURE-OR</a> dataset, there are 1,000,000 images of 100 objects with varying size, color, and texture, captured with multiple devices in different setups. The majority of images in the dataset were acquired with smartphones and tested with off-the-shelf applications to benchmark the recognition performance of devices and applications that are used in our daily lives. Please refer to our <a href="https://github.com/olivesgatech/CURE-OR">GitHub page</a> for code, papers, and more information. Some data specifications are provided below:</p> <p><strong>Image Name Format : </strong></p> <p>"backgroundID_deviceID_objectOrientationID_objectID_challengeType_challengeLevel.jpg"</p> <p><strong>Background ID: </strong></p> <p>1: White 2: Texture 1 - living room 3: Texture 2 - kitchen 4: 3D 1 - living room 5: 3D 2 – office</p> <p><strong>Object Orientation ID: </strong></p> <p>1: Front (0 º) 2: Left side (90 º) 3: Back (180 º) 4: Right side (270 º) 5: Top</p> <p><strong>Object ID:</strong></p> <p> 1-100</p> <p><strong>Challenge Type:</strong> </p> <p>No challenge 02: Resize 03: Underexposure 04: Overexposure 05: Gaussian blur 06: Contrast 07: Dirty lens 1 08: Dirty lens 2 09: Salt & pepper noise 10: Grayscale 11: Grayscale resize 12: Grayscale underexposure 13: Grayscale overexposure 14: Grayscale gaussian blur 15: Grayscale contrast 16: Grayscale dirty lens 1 17: Grayscale dirty lens 2 18: Grayscale salt & pepper noise</p> <p><strong>Challenge Level: </strong></p> <p>A number between [0, 5], where 0 indicates no challenge, 1 the least severe and 5 the most severe challenge. Challenge type 1 (no challenge) and 10 (grayscale) has a level of 0 only. Challenge types 2 (resize) and 11 (grayscale resize) has 4 levels (1 through 4). All other challenges have levels 1 to 5.</p>
H.I.D.R.A.: A Hierarchical, Interactive and Dynamic Recognition Architecture for Product Categorization
<p>The Hierarchical, Interactive and Dynamic Recognition Architecture (H.I.D.R.A.) for Product Categorization is a new intelligent system architecture developed by Elo7 to easily evolve its category tree and automatically classify millions of products, thus improving the page ranking of our marketplace.</p>
Supplementary Videos: The N-Terminal Helix-Turn-Helix Motif of Transcription Factors MarA and Rob Drives DNA Recognition
<p>Supplementary Movies associated with the following work: "The N-Terminal Helix-Turn-Helix Motif of Transcription Factors MarA and Rob Drives DNA Recognition", available as a preprint on chemRxiv: https://chemrxiv.org/articles/preprint/The_N-Terminal_Helix-Turn-Helix_Motif_of_Transcription_Factors_MarA_and_Rob_Drives_DNA_Recognition/12195372 </p>
Fig. 4 in Photo-identification as a technique for recognition of individual fish: a test with the freshwater armored catfish Rineloricaria aequalicuspis Reis & Cardoso, 2001 (Siluriformes: Loricariidae)
Fig. 4. Percentage of correct matches (a) and expended minutes (b) between naked-eye and computer-assisted field test photo-identification for individual recognition of Rineloricaria aequalicuspis (n = 9). Boxplots show median (central thicker line), first and third quartile (box limits), 95% confidence interval of median (whiskers), and outliers.
Fig. 3 in Photo-identification as a technique for recognition of individual fish: a test with the freshwater armored catfish Rineloricaria aequalicuspis Reis & Cardoso, 2001 (Siluriformes: Loricariidae)
Fig. 3. Variation in number, shape, size and organization of the bony plates covering the abdominal surface of six different Rineloricaria aequalicuspis individuals with more than 10 cm total length. These are examples of photographs taken during the field test. (a) 175 mm TL; (b) 138 mm TL; (c) 156 mm TL; (d) 145 mm TL; (e) 141 mm TL; (f) 151 mm TL.
Fig. 2 in Photo-identification as a technique for recognition of individual fish: a test with the freshwater armored catfish Rineloricaria aequalicuspis Reis & Cardoso, 2001 (Siluriformes: Loricariidae)
Fig. 2. Diagram showing the steps employed to assess the performance of photo-identification technique in laboratory (a) and field (b) conditions for Rineloricaria aequalicuspis.
Fig. 1 in Photo-identification as a technique for recognition of individual fish: a test with the freshwater armored catfish Rineloricaria aequalicuspis Reis & Cardoso, 2001 (Siluriformes: Loricariidae)
Fig. 1. Lateral, dorsal and ventral views of a Rineloricaria aequalicuspis individual (110 mm TL). Ventral view shows the arrangement of the abdominal plates. Photograph courtesy of L. R. Malabarba.
Datasets used in the thesis "Contributions to High-Dimensional Pattern Recognition"
<p>Datasets used in the thesis "Contributions to High-Dimensional Pattern Recognition" and related publications.</p>
ICDAR 2015 Competition HTRtS: Handwritten Text Recognition on the tranScriptorium Dataset
<p>This dataset comprises the dataset used for the ICDAR 2015 Competition on Handwritten Text Recognition on the tranScriptorium Dataset. The handwritten images for this contest were drawn from the English “Bentham collection” dataset used in the TRAN SCRIPTORIUM project. The selected data has been written by several hands and entails significant variabilities and difficulties regarding the quality of text images, writing styles and crossed-out text. This contest is clearly more difficult than the the first edition both for training and for testing. A portion of the training dataset and the full test dataset were provided in the form of carefully segmented line images, along with the corresponding transcripts. Another portion of the training dataset was provided as raw images and their corresponding transcripts at region level.<br> </p> <p>ICDAR 2015 competition HTRtS: handwritten text recognition on the tranScriptorium dataset<br> JA Sánchez, AH Toselli, V Romero, E Vidal. In International Conference on Document Analysis and Recognition (ICDAR), pp. 1166-1170, 2015.</p>
Research data supporting "Plasmonic chirality imprinting on nucleobase-displaying supramolecular nanohelices via metal-nucleobase recognition"
<p>This file contains the raw research data supporting the publication:</p> <p>Y. Lin<em> et al</em>., Plasmonic chirality imprinting on nucleobase-displaying supramolecular nanohelices via metal-nucleobase recognition, Angew. Chem. Int. Ed. 2017, DOI: 10.1002/anie.201610976.</p> <p> </p>
Train-B dataset for ICDAR2017 Competition on Handwritten Text Recognition on the READ Dataset (ICDAR2017 HTR). Batch 1 and Batch 2.
<p>Train-B Dataset. Dataset of pages without any layout or text line information. The corresponding transcripts are provided at page level with line breaks. It has 10k pages, though for convenience it is divided into two 5k page batches. This information is provided in PAGE format. </p> <p>This dataset is complementary to this other dataset:</p> <p>https://zenodo.org/record/439807#.WOIBZ3WLSkA</p> <p>More information at:</p> <p>https://scriptnet.iit.demokritos.gr/competitions/~icdar2017htr/</p> <p> </p>
Train-A dataset for ICDAR2017 Competition on Handwritten Text Recognition on the READ Dataset (ICDAR2017 HTR)
<p>Train-A Dataset of pages with manually revised baselines and the corresponding transcripts associated to them. This batch is small, 50 pages. Please, keep in mind that only the baselines have been manually corrected, The polygons associated to each line have not been manually reviewed. </p> <p>This dataset is complementary to this other dataset:</p> <p>https://zenodo.org/record/439811#.WOIF9HWLSkA</p> <p>More information at:</p> <p>https://scriptnet.iit.demokritos.gr/competitions/~icdar2017htr/</p>
MatSim Dataset and benchmark for one-shot visual materials and textures recognition
<p><strong>The MatSim Dataset and benchmark</strong></p> <p>Synthetic dataset and real images benchmark for visual similarity recognition of materials and textures.</p> <p>MatSim: a synthetic dataset, a benchmark, and a method for computer vision-based recognition of similarities and transitions between materials and textures focusing on identifying any material under any conditions using one or a few examples (one-shot learning).</p> <p>Based on the paper: <a href="https://arxiv.org/pdf/2212.00648.pdf">One-shot recognition of any material anywhere using contrastive learning with physics-based rendering</a></p> <p> </p> <p><strong>Benchmark_MATSIM.zip: </strong>contain the benchmark made of real-world images as described in the paper</p> <p><strong>Dataset Generation Scripts.zip: </strong>Contain the Blender (4.1) Python scripts used for generating the dataset<br><br><a href="https://zenodo.org/record/7390166/files/MatSim_object_train_split_1.zip?download=1"><strong>MatSim_object_train_split_1,2,3....zip:</strong> </a>Contain a subset of the synthetics dataset for images of CGI images materials on random objects as described in the paper.</p> <p><strong>MatSimTrainObjectsNearField_.zip </strong>Contain train sets with near fieldlight sources</p> <p><strong><a href="https://zenodo.org/record/7390166/files/MatSim_Vessels_Train_1.zip?download=1">MatSim_Vessels_Train_1,2,3....zip </a></strong><a href="https://zenodo.org/api/files/020f90b2-7c41-44ad-86e3-69257884a569/MatSim_object_train_split_1.zip"><strong>:</strong> </a>Contain a subset of the synthetics dataset for images of CGI images materials inside transparent containers as described in the paper.<br><br><strong>*Note: these are subsets of the dataset; the full dataset can be found at:</strong><br><a href="https://e1.pcloud.link/publink/show?code=kZIiSQZCYU5M4HOvnQykql9jxF4h0KiC5MX">https://e1.pcloud.link/publink/show?code=kZIiSQZCYU5M4HOvnQykql9jxF4h0KiC5MX</a></p> <p>or<br><a href="https://icedrive.net/s/A13FWzZ8V2aP9T4ufGQ1N3fBZxDF">https://icedrive.net/s/A13FWzZ8V2aP9T4ufGQ1N3fBZxDF</a></p> <p> </p>
Figures 3–6 in Recognition of Chyrsobothris thoracica guadeloupensis Descarpentries, 1981 at the species level (Coleoptera: Buprestidae) Norman E. Woodley
Figures 3–6. Male genitalia of Chrysobothris species. 3) C. thoracica from Guánica, Puerto Rico, dorsal view. 4) Same specimen, ventral view. 5) C. guadeloupensis from Gourbeyre, Guadeloupe, dorsal view. 6) Same specimen, ventral view.
Figure 78 in A preliminary report on the World species of Bemisia Quaintance and Baker and its congeners (Hemiptera: Aleyrodidae) with a comparative analysis of morphological variation and its role in the recognition of species Raymond Gill
Figure 78. Bemisia afer complex, Madeira, Santana, Faja do Niguiera, 15-XII-92, ex. Myrica toya, F. Aguiar, coll.
Figure 77 in A preliminary report on the World species of Bemisia Quaintance and Baker and its congeners (Hemiptera: Aleyrodidae) with a comparative analysis of morphological variation and its role in the recognition of species Raymond Gill
Figure 77. Bemisia afer complex, Madeira, Faja do Penedo, 20.iii.92, ex. Marcetella madeirensis, F. Aguiar, coll., #C136.
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.