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9,302 results for “DETECT”
ICDAR 2019 Competition on Table Detection and Recognition (cTDaR)
<p>The aim of this competition is to evaluate the performance of state of the art methods for table detection (TRACK A) and table recognition (TRACK B). For the first track, document images containing one or several tables are provided. For TRACK B two subtracks exist: the first subtrack (B.1) provides the table region. Thus, only the table structure recognition must be performed. The second subtrack (B.2) provides no a-priori information. This means, the table region and table structure detection has to be done. The Ground Truth is provided in a similar format as for the ICDAR 2013 competition (see [2]):</p> <p><?xml version="1.0" encoding="UTF-8"?></p> <p><<strong>document</strong> filename='filename.jpg'></p> <p> <<strong>table</strong> id='Table_1540517170416_3'></p> <p><strong> <Coords points="180,160 4354,160 4354,3287 180,3287"/></strong></p> <p> <<strong>cell</strong> id='TableCell_1540517477147_58' <strong>start-row</strong>='0' <strong>start-col</strong>='0' <strong>end-row</strong>='1' <strong>end-col</strong>='2'></p> <p> <<strong>Coords</strong> <strong>points</strong>="180,160 177,456 614,456 615,163"/></p> <p> </cell></p> <p> ...</p> <p> </table></p> <p> ...</p> <p></document></p> <p> </p> <p>The difference to Gobel et al. [2] is the Coords tag which defines a table/cell as a polygon specified by a list of coordinates. For B.1 the table and its coordinates is given together with the input image.</p> <p>Important Note:</p> <p>For the modern dataset, the convex hull of the content describes a cell region. For the historical dataset, it is requested that the output region of a cell is the cell boundary. This is necessary due to the characteristics of handwritten text, which is often overlapping with different cells.</p> <p>See also: http://sac.founderit.com/tasks.html</p> <p>The evaluation tool is available at github: https://github.com/cndplab-founder/ctdar_measurement_tool</p>
Bats actively use leaves as specular reflectors to detect acoustically camouflaged prey
<p>Measured target strength from 541 positions for 5 different frequency bands. Bat positions in incidence angles of for 33 flight paths.</p>
Tissue blocks for EBV detection in stomach cancer
<p>Tissue blocks from hematoxylin and eosin (H&E) images of stomach adenocarcinoma samples (STAD) from our validation cohort</p> <p>They are grouped into four classes:</p> <ul> <li>false negative: true EBV+ patient, block predicted to be EBV-</li> <li>false positive: true EBV- patient, block predicted to be EBV+</li> <li>true negative: true EBV- patient, block predicted to be EBV-</li> <li>true positive: true EBV+ patient, block predicted to be EBV+</li> </ul> <p>all blocks are 224 x 224 px, corresponding to 256x256 µm</p> <p>The blocks represent only a subset of the full validation cohort, selected as follows</p> <ol> <li>from each patient, only a maximum of 50 randomly drawn blocks was used</li> <li>we saved the upper and lower quartile (sorted by EBV score)</li> <li>we then grouped into true and false positive (high EBV score) and negative (low EBV score), respectively</li> </ol>
A collection of spectral descriptors for the detection of five polymer types
<p>This set of spectral descriptors was designed for the purpose of detecting the polymers polyethylene (PE), polypropylene (PP), polystyrene (PS), polyacrylonitrile (PAN) and poly(methyl methacrylate) (PMMA).</p> <p>The descriptors can be applied to hyperspectral image datasets using the software tool <a href="http://imagelab.at">Epina ImageLab</a>.</p>
MTG-Audio Problems Detection on Sound Collections
<p>Manual annotation for the Audio Tagging Competition 2019 (<a href="https://www.kaggle.com/c/freesound-audio-tagging-2019">https://www.kaggle.com/c/freesound-audio-tagging-2019</a>) for audio problems described in Victor Badenas' Thesis from <a href="https://github.com/pirulok02/MTG-Audio-Problems-Detection">https://github.com/pirulok02/MTG-Audio-Problems-Detection</a></p>
Mining the UK Web Archive for Semantic Change Detection (Dataset)
<p>The dataset that was used and released with the RANLP 2019 paper, titled "Mining the UK Web Archive for Semantic Change Detection" (see <a href="https://github.com/adtsakal/Semantic_Change">https://github.com/adtsakal/Semantic_Change</a>). It contains annual word2vec representations of more than 47K words over the period 2000-2013, along with a list of 65 words with known semantic change over the same time period. </p>
A Self-Adaptive Deep Learning Method for Automated Eye Laterality Detection Based on Color Fundus Photography
<p>The data for "A Self-Adaptive Deep Learning Method for Automated Eye Laterality Detection Based on Color Fundus Photography", including original image data and labels, DL model files, training logs and validation results.</p> <p><strong>Content</strong>:</p> <p>- <strong>Ex1_Preprocessing</strong> # the first experiment: preprocessing methods comparison</p> <p> - <strong>history</strong> # validation results of different metrics of models </p> <p> - <strong>log</strong> # training record of models. Read by TensorBoard</p> <p> - <strong>model</strong> # model file. Read by Keras</p> <p>- <strong>Ex2_Self-adaptive model</strong> # the second experiment: development of eye laterality detection model</p> <p> - <strong>data</strong> # serialized image data, including data and ground-truth label. Read by Python Pickle package.</p> <p> - <strong>Xy.h5</strong> # training data</p> <p> - <strong>test_Xy.h5</strong> # testing data</p> <p> - <strong>history</strong> # validation results of different metrics of models </p> <p> - <strong>log</strong> # training record of models. Read by TensorBoard</p> <p> - <strong>model</strong> # model file. Read by Keras</p> <p> - <strong>with_adaptive</strong> # with self-adaptive strategy</p> <p> - <strong>without_adaptive</strong> # without self-adaptive strategy</p> <p>For code review, please see: <a href="https://github.com/keepgallop/Eye-laterality-detection">https://github.com/keepgallop/Eye-laterality-detection</a></p> <p>For more information, please contact liuchi_email@foxmail.com</p> <p> </p> <p> </p> <p> </p>
Dataset: Occupancy Detection, Tracking, and Estimation Using a Vertically Mounted Depth Sensor
<p>Occupancy detection, tracking, and estimation has a wide range of applications including improving building energy efficiency, safety, and security of the occupants. As depth sensors are getting cheaper, they offer a viable solution to estimate occupancy accurately in a non-privacy invasive manner. Even though there are publicly available depth datasets, they do not consider placing the sensor in the ceiling looking downwards to estimate occupancy. We deployed four Kinect for XBOX One in four CMU classrooms and conference rooms for a period of four weeks in 2017 and collected over 6 TB of depth data. We annotate this huge dataset by labelling bounding boxes around occupants and release the annotated dataset. </p> <p>A sample of the dataset can be found here: <a href="https://doi.org/10.5281/zenodo.3457385">https://doi.org/10.5281/zenodo.3457385</a></p>
Database for comparison between Hybrid Capture and PCR techniques for HPV-HR detection
<p>This database displays the results from the HPV-HR detection with the gold standard technique Hybrid Capture 2 (HC2) and different PCR-based techniques. These results are compared in order to determine the degree of agreement between theses techniques. </p>
An exploratory study (with and without time pressure) using mouse dynamics to detect faking-good behavior in the MMPI-2 and PPI-R validity scales
<p>Please find here the dataset generated and analyzed during the study entitled "Can mouse dynamics detect faking-good behavior in personality questionnaires? An exploratory study (with and without time pressure) using the MMPI-2 and PPI-R validity scales". Moreover, here you can find the source code of the experiment to execute the task using MouseTracker software, the code of the statistical analysis and a file containing the instructions to replicate ML model results reported in the original paper.</p>
Repository of speech features from speakers with and without Parkinson's Disease. Neurovoz - Rasta PLP - V2 - Scientific Reports Publication: Phonetic relevance and phonemic grouping of speech in the automatic detection of Parkinson's Disease
<p>This repository contains the Rasta-PLP features of six different speech recordings (sentences) from Neurovoz corpus (47 parkinsonian and 32 control speakers whose mother tongue is Spanish Castillian.)<br> Number of PLP coefficients: [6, 8, 10, 12, 14, 16, 18, 20].<br> Delta coefficients: Yes<br> Delta Delta coefficients: Yes<br> Sampling rate: 16 kHz<br> Frame size: 15 ms<br> Frame overlapping: 50%</p> <p>This subset of the Neurovoz corpus was recorded between 2015 and 2017 by Universidad Politécncia de Madrid and Hospital General Universitario Gregorio Marañón.</p> <p>This version includes the same files as the previous version and information about UPDRS, H&Y, years since diagnosis and age of each participant.</p> <p>The sentences were:</p> <p>BARBAS: "Cuando las barbas de tu vecino veas pelar, pon las tuyas a remojar"</p> <p>CALLE: "De la calle vendrá quien de tu casa te echará"</p> <p>DIABLO: " Cuando el diablo no sabe qué hacer, con el rabo mata moscas "</p> <p>PETACA BLANCA: " La petaca blanca es mía"</p> <p>PIDIO: "No pidas a quien pidió ni sirvas a quien sirvió"</p> <p>SOMBRA: " El que a buen árbol se arrima, buena sombra le cobija "</p> <p> </p> <p>How to cite:<br> [1] Moro-Velazquez, L., Gomez-Garcia, J. A., Godino-Llorente, J. I., Grandas-Perez, F., Shattuck-Hufnagel, S. Yagüe-Jimenez, V., and Dehak, N. (2019). Phonetic relevance and phonemic grouping of speech in the automatic detection of Parkinson’s disease.Scientific reports 9, 19066.</p> <p><br> [2] Moro-Velazquez, L., Gomez-Garcia, J. A., Godino-Llorente, J. I., Villalba, J., Rusz, J., Shattuck-Hufnagel, S. and Dehak, N. (2019). A forced Gaussians based methodology for the differential evaluation of Parkinson's Disease by means of speech processing. Biomedical Signal Processing and Control, 48, 205-220.</p> <p>BibTeX:</p> <pre><code>@article{moro2019phonetic, title={Phonetic relevance and phonemic grouping of speech in the automatic detection of Parkinson's Disease}, author={Moro-Velazquez, Laureano and Gomez-Garcia, Jorge A. and Godino-Llorente, Juan I. and Grandas-Perez, Francisco and Shattuck-Hufnagel, Stefanie and Yague-Jimenez, Virginia and Dehak, Najim}, journal={Scientific Reports}, volume={9}, pages={19066}, year={2019}, publisher={Nature Research Publishing} } @article{moro2019forced, title={A forced Gaussians based methodology for the differential evaluation of Parkinson's Disease by means of speech processing}, author={Moro-Velazquez, Laureano and Gomez-Garcia, Jorge Andres and Godino-Llorente, Juan Ignacio and Dehak, Najim}, journal={Biomedical Signal Processing and Control}, pages={205--220}, volume={48}, year={2019}, publisher={Elsevier} } </code></pre> <p> </p>
Figure 3 in Detection of helminth parasites in commercialized turtles: threats to native Testudines in northeast India
Figure 3. Dichelyne species: (A) Esophagus, lateral view, and (B) Tail, lateral view.
Fig. 1 in Detection of rodent-borne parasitic pathogens of wild rats in Serdang, Selangor, Malaysia: A potential threat to human health
Fig. 1. The study area, Universiti Putra Malaysia showing the study sites circled in black.
Fig. 1 in Taeniid cestodes in Tibetan foxes (Vulpes Ferrilata) detected by copro-PCR: Applications and challenges
Fig. 1. (continued).
Fig. 2 in Mosquito identification and haemosporidian parasites detection in the enclosure of the African penguins (Spheniscus demersus) at the SANBI zoological garden
Fig. 2. The single UV-light trap used to collect mosquito samples.
Out of the blue: Detection of a unique highly pathogenic avian influenza virus of subtype H7N5 in Germany: Data sets on phylogenetic analyses
<div> <p><span>In June 2024, a highly pathogenic avian influenza virus of subtype H7N5 was detected in a single laying hen holding in Germany. Closest relatives of the eight viral genome segments were among recent low pathogenic (LP) viruses from Asia and Europe. No further detections of this unique virus or its presumed LP precursor have been made in poultry or wild birds.</span></p> </div>
Fig. 2 in Molecular detection and characterization of a novel Theileria genotype in Dama Gazelle (Nanger dama)
Fig. 2. The clade credibility values of phylogram generated from bayesian analysis.
Fig. 7. L 3 in Detection of Spirocerca lupi and an unknown Trichinella-like nematode in raccoon (Procyon lotor)
Fig. 7. L 3 of Spirocerca lupi: These larvae occurred as accidental findings.
COCO dataset and neural network weights for micro-FTIR particle detection on filters.
<h3>The IMPTOX project has received funding from the EU's H2020 framework programme for research and innovation under grant agreement n. 965173. Imptox is part of the European MNP cluster on human health.</h3> <p>More information about the project <a href="https://www.imptox.eu/en/">here</a>.</p> <p><strong>Description:</strong> This repository includes the trained weights and a custom COCO-formatted dataset used for developing and testing a Faster R-CNN R_50_FPN_3x object detector, specifically designed to identify particles in micro-FTIR filter images.</p> <p><strong>Contents:</strong></p> <ol> <li> <p><strong>Weights File (<code>neuralNetWeights_V3.pth</code>):</strong></p> <ul> <li>Format: .pth</li> <li>Description: This file contains the trained weights for a Faster R-CNN model with a ResNet-50 backbone and a Feature Pyramid Network (FPN), trained for 3x schedule. These weights are specifically tuned for detecting particles in micro-FTIR filter images.</li> </ul> </li> <li> <p><strong>Custom COCO Dataset (<code>uFTIR_curated_square.v5-uftir_curated_square_2024-03-14.coco-segmentation.zip</code>):</strong></p> <ul> <li>Format: .zip</li> <li>Description: This zip archive contains a custom COCO-formatted dataset, including JPEG images and their corresponding annotation file. The dataset consists of images of micro-FTIR filters with annotated particles.</li> <li>Contents: <ul> <li><strong>Images:</strong> JPEG format images of micro-FTIR filters.</li> <li><strong>Annotations:</strong> A JSON file in COCO format providing detailed annotations of the particles in the images.</li> </ul> </li> <li>Management: The dataset can be managed and manipulated using the <a href="https://pypi.org/project/pycocotools/">Pycocotools</a> library, facilitating easy integration with existing COCO tools and workflows.</li> </ul> </li> </ol> <p><strong>Applications:</strong> The provided weights and dataset are intended for researchers and practitioners in the field of microscopy and particle detection. The dataset and model can be used for further training, validation, and fine-tuning of object detection models in similar domains.</p> <p><strong>Usage Notes:</strong></p> <ul> <li>The <code>neuralNetWeights_V3.pth</code> file should be loaded into a PyTorch model compatible with the Faster R-CNN architecture, such as Detectron2.</li> <li>The contents of <code>uFTIR_curated_square.v5-uftir_curated_square_2024-03-14.coco-segmentation.zip</code> should be extracted and can be used with any COCO-compatible object detection framework for training and evaluation purposes.</li> <li>Code can be found on the related <a href="https://github.com/ThibaultSchowing/IMPTOX" target="_blank" rel="noopener">Github repository.</a></li> </ul> <p> </p>
Magnetic particles (Fe3O4) magnify ion transfer processes at the electrified liquid-liquid interface. Case study: Levamisole detection.
<p>Data set for the publication Magnetic particles (Fe3O4) magnify ion transfer processes at the electrified liquid-liquid interface. Case study: Levamisole detection. </p>
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.