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Dataset results
15 results for “machine vision”
Bridging the gap between single nanoparticle imaging and global electrochemical response by correlative microscopy assisted by machine vision
<p>The data in this repository corresponds to experimental data: linear sweep voltammetry, optical movie and the database of the SEM images. They support the findings of a study discussed in the article by Godeffroy et al. published in Small Methods with the doi: http:/doi.org/10.1002/smtd.202200659. The data analysis to reproduce the results presented in the article has been carried out by homemade Python program routines also provided in this repository. The descirption of each routine is also provided in a text file.</p>
Brain Tumor MR Image Data Set For Machine Vision Approach for Brain Tumor Classification using Multi Features Dataset
<p>The uploaded dataset contains the brain tumor MRI dataset. The dataset has been collected form the Bahawal Victoria Hospital, Bahawalpur, Pakistan. This dataset is an authorized MRI brain tumor dataset. Is has been authorized from the expert Radiologists of the Bahawal Victoria Hospital <a href="https://www.qamc.edu.pk/administration/2">BVH</a>. The dataset consists of three brain tumor types, namely adenomas, meningioma and glioma. it is only for academic, educational and experimental purpose. no other usage will be owned or any liability will be accepted by the authors.</p>
Data from: Combining Unity with machine vision to create low latency, flexible, and simple virtual realities
Open the record for dataset details and reuse information.
Machine vision for vial-positioning detections towards safe automation of material synthesis
<p>This repository contains an DenseSSD's datasets that predicts vial-positioning detection using object detection techniques. DenseSSD can play vital roles in addressing these safety issues as well as can alert to user's messenger to notify and fix safety issues as soon as possible.</p>
Evaluation of 3D Machine-vision Image Guided Surgery Spine Navigation
ClinicalTrials.gov study NCT03968965. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Automatic titration detection method of organic matter content based on machine vision
Open the record for dataset details and reuse information.
Traffic images captured from UAVs for use in training Machine Vision Algorithms for traffic management
<p><strong>If you use this dataset please cite this paper: Bemposta Rosende, S.; Ghisler, S.; Fernández-Andrés, J.; Sánchez-Soriano, J. Dataset: Traffic Images Captured from UAVs for Use in Training Machine Vision Algorithms for Traffic Management. Data 2022, 7, 53. https://doi.org/10.3390/data7050053</strong></p> <p>A dataset of road traffic images taken from unmanned aerial vehicles (UAV) with the purpose of being used to train artificial vision algorithms, among which those based on convolutional neural networks stand out. </p> <p>Dataset is available and accessible in order to improve the performance of road traffic vision and management systems due to the lack of resources in this specific domain. The full description of the characteristics of the dataset, as well as its components and format, can be found here: <a href="https://www.mdpi.com/2306-5729/7/5/53">https://www.mdpi.com/2306-5729/7/5/53</a></p> <p>The dataset format is YOLO. Relevant data about the dataset:</p> <table> <tbody> <tr> <td> <p><strong>Scenes</strong></p> </td> <td> <p><strong>Frames</strong></p> </td> <td> <p><strong>Targets</strong></p> </td> <td> <p><strong>Cars</strong></p> </td> <td> <p><strong>Motorbikes</strong></p> </td> </tr> <tr> <td> <p>Regional road</p> </td> <td> <p>4.500</p> </td> <td> <p>24.858</p> </td> <td> <p>14.577</p> </td> <td> <p>10.281</p> </td> </tr> <tr> <td> <p>Urban intersection</p> </td> <td> <p>2.462</p> </td> <td> <p>10.759</p> </td> <td> <p>10.759</p> </td> <td> <p>0</p> </td> </tr> <tr> <td> <p>Rural road</p> </td> <td> <p>1.292</p> </td> <td> <p>746</p> </td> <td> <p>746</p> </td> <td> <p>0</p> </td> </tr> <tr> <td> <p>Split roundabout</p> </td> <td> <p>2.297</p> </td> <td> <p>3.107</p> </td> <td> <p>3.107</p> </td> <td> <p>0</p> </td> </tr> <tr> <td> <p>Roundabout (Far)</p> </td> <td> <p>1.814</p> </td> <td> <p>71.819</p> </td> <td> <p>64.844</p> </td> <td> <p>6.975</p> </td> </tr> <tr> <td> <p>Roundabout (Near)</p> </td> <td> <p>3.997</p> </td> <td> <p>4.4039</p> </td> <td> <p>43.569</p> </td> <td> <p>470</p> </td> </tr> <tr> <td> <p><strong>Total</strong></p> </td> <td> <p><strong>15.070</strong></p> </td> <td> <p><strong>155.328</strong></p> </td> <td> <p><strong>137.602</strong></p> </td> <td> <p><strong>17.726</strong></p> </td> </tr> </tbody> </table>
Datasets for: A Study on Machine Vision Techniques...
<p>Datasets for: A Study on Machine Vision Techniques...</p>
MMV_Im2Im: An Open Source Microscopy Machine Vision Toolbox for Image-to-Image Transformation
<p>This dataset contains trained deep learning models and sample data for the manuscript "MMV_Im2Im: An Open Source Microscopy Machine Vision Toolbox for Image-to-Image Transformation". Please find the software and more information including tutorials here: https://github.com/MMV-Lab/mmv_im2im.</p><p> </p><p>sample_data.zip includes the following datasets:</p><ul><li>Labelfree prediction of nuclear structure from 2D/3D brighteld images<ul><li>2D<ul><li><a href="https://zenodo.org/record/6139958#.Y78QJKrMLtU">https://zenodo.org/record/6139958#.Y78QJKrMLtU</a></li><li><a href="https://zenodo.org/record/6140064#.Y78YeqrMLtU">https://zenodo.org/record/6140064#.Y78YeqrMLtU</a></li><li>Both repositories have a Creative Commons Attribution 4.0 International License</li></ul></li><li>3D<ul><li><a href="https://open.quiltdata.com/b/allencell/packages/aics/hipsc_single_cell_image_dataset">https://open.quiltdata.com/b/allencell/packages/aics/hipsc_single_cell_image_dataset</a></li><li>Terms of use: <a href="https://www.allencell.org/terms-of-use.html">https://www.allencell.org/terms-of-use.html</a></li><li>"Your use of the Content, including creation of derivative works of the services, data and tools, must be for research or other noncommercial purposes unless it is otherwise set forth in these Terms or agreed to in writing by the Allen Institute."</li></ul></li></ul></li><li>2D semantic segmentation of tissues from H&E images<ul><li><a href="https://www.kaggle.com/datasets/sani84/glasmiccai2015-gland-segmentation">https://www.kaggle.com/datasets/sani84/glasmiccai2015-gland-segmentation</a></li><li>"<strong>The dataset used in this competition is provided for research purposes only. Commercial uses are not allowed.</strong><br>If you intend to publish research work that uses this dataset, you must cite our review paper to be published after the competition"</li></ul></li><li>Instance segmentation<ul><li>2D<ul><li><a href="https://bbbc.broadinstitute.org/BBBC010">https://bbbc.broadinstitute.org/BBBC010</a></li><li>Terms of use: <a href="https://bbbc.broadinstitute.org/">https://bbbc.broadinstitute.org/</a></li><li>"Researchers are encouraged to use these image sets as reference points when developing, testing, and publishing new image analysis algorithms for the life sciences."</li></ul></li><li>3D<ul><li><a href="https://open.quiltdata.com/b/allencell/packages/aics/hipsc_single_cell_image_dataset">https://open.quiltdata.com/b/allencell/packages/aics/hipsc_single_cell_image_dataset</a></li><li>Terms of use: <a href="https://www.allencell.org/terms-of-use.html">https://www.allencell.org/terms-of-use.html</a></li><li>"Your use of the Content, including creation of derivative works of the services, data and tools, must be for research or other noncommercial purposes unless it is otherwise set forth in these Terms or agreed to in writing by the Allen Institute."</li></ul></li></ul></li><li>Compare semantic segmentation and instance segmentation<ul><li><a href="https://open.quiltdata.com/b/allencell/packages/aics/hipsc_single_cell_image_dataset">https://open.quiltdata.com/b/allencell/packages/aics/hipsc_single_cell_image_dataset</a></li><li>Terms of use: <a href="https://www.allencell.org/terms-of-use.html">https://www.allencell.org/terms-of-use.html</a></li><li>"Your use of the Content, including creation of derivative works of the services, data and tools, must be for research or other noncommercial purposes unless it is otherwise set forth in these Terms or agreed to in writing by the Allen Institute."</li></ul></li><li>Unsupervised semantic segmentation<ul><li><a href="https://open.quiltdata.com/b/allencell/packages/aics/hipsc_single_cell_image_dataset">https://open.quiltdata.com/b/allencell/packages/aics/hipsc_single_cell_image_dataset</a></li><li>Terms of use: <a href="https://www.allencell.org/terms-of-use.html">https://www.allencell.org/terms-of-use.html</a></li><li>"Your use of the Content, including creation of derivative works of the services, data and tools, must be for research or other noncommercial purposes unless it is otherwise set forth in these Terms or agreed to in writing by the Allen Institute."</li></ul></li><li>Generating synthetic images<ul><li><a href="https://open.quiltdata.com/b/allencell/packages/aics/hipsc_single_cell_image_dataset">https://open.quiltdata.com/b/allencell/packages/aics/hipsc_single_cell_image_dataset</a></li><li>Terms of use: <a href="https://www.allencell.org/terms-of-use.html">https://www.allencell.org/terms-of-use.html</a></li><li>"Your use of the Content, including creation of derivative works of the services, data and tools, must be for research or other noncommercial purposes unless it is otherwise set forth in these Terms or agreed to in writing by the Allen Institute."</li></ul></li><li>Image denoising<ul><li><a href="https://csbdeep.bioimagecomputing.com/scenarios/">https://csbdeep.bioimagecomputing.com/scenarios/</a></li><li>Two datasets: "Denoising in 3D (Planaria nuclei)" and "Denoising in 3D (Tribolium nuclei)"</li><li>Terms of use: <a href="http://csbdeep.bioimagecomputing.com/">http://csbdeep.bioimagecomputing.com/</a></li><li>"The entire CSBDeep toolbox is fully open source and intended to be used from either Python or <a href="https://fiji.sc">Fiji</a>."</li></ul></li><li>Imaging modality transformation<ul><li><a href="https://zenodo.org/record/4624364#.Y9bWOoHMIqJ">https://zenodo.org/record/4624364#.Y9bWOoHMIqJ</a></li><li>Two datasets: "Confocal_2_STED.zip" (Microtubule and Nuclear_Pore_complex)</li><li>Repository has a Creative Commons Attribution 4.0 International License</li></ul></li><li>Staining transformation:<ul><li><a href="https://zenodo.org/record/4751737#.Y9gbv4HMLVZ">https://zenodo.org/record/4751737#.Y9gbv4HMLVZ</a></li><li>Dataset "BC-DeepLIIF_Training_Set.zip" and "BC-DeepLIIF_Validation_Set.zip"</li><li>Repository has a Creative Commons Attribution 4.0 International License</li></ul></li></ul><p> </p>
An explainable deep machine vision framework for plant stress phenotyping
<p>This is a companion dataset to the paper 'An explainable deep machine vision framework for plant stress phenotyping' by Ghosal et al., published in 2018 in the Proceedings of the National Academy of Sciences. The full citation is:</p> <p>Ghosal, Sambuddha, David Blystone, Asheesh K. Singh, Baskar Ganapathysubramanian, Arti Singh, and Soumik Sarkar. 'An explainable deep machine vision framework for plant stress phenotyping.' Proceedings of the National Academy of Sciences 115, no. 18 (2018): 4613-4618.</p>
A Study of Detection of Paroxysmal Events Utilizing Computer Vision and Machine Learning (USF)
ClinicalTrials.gov study NCT06705439. IPD Sharing: NO. Countries: 1. Publications: 0.
A Study of Detection of Paroxysmal Events Utilizing Computer Vision and Machine Learning - Nelli
ClinicalTrials.gov study NCT05606575. IPD Sharing: NO. Countries: 1. Publications: 0.
A Study of Detection of Paroxysmal Events Utilizing Computer Vision and Machine Learning
ClinicalTrials.gov study NCT04738552. IPD Sharing: NO. Countries: 1. Publications: 0.
Machine Vision Based MDS-UPDRS III Machine Rating
ClinicalTrials.gov study NCT05906719. IPD Sharing: NO. Countries: 1. Publications: 0.
Using Machine Learning to Adapt Visual Aids for Patients With Low Vision
ClinicalTrials.gov study NCT04892316. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.