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Dataset results
13 results for “edge detection”
Detecting edge effects of geese grazing at the boundary of woodland and grassland
<p>The presence of geese on different areas of lawn was estimated by the length of droppings on the lawn. Geese defecate frequently and seemingly indiscriminately. Counting dropping is a well-known method for estimating their density on areas of land (Owen, 1971). However, we found it difficult to distinguish individual defecation events as the dropping tend to break apart as they are released. Therefore, we measured the total length of dropping in an area. Geese dropping are more or less cylindrical and we consider a measure related to the volume of droppings is more reliable than a count of their number.</p> <p>Observations were conducted in July 2014 and March and April 2015 at Meise Botanic Garden, Meise, Belgium. Rectangular plots were laid out perpendicular to a woodland-lawn boundary on sections of a Botanic Garden frequently used by geese. These plots are detailed in file DroppingsPlots.csv. The sites for these plots were chosen because they were well separated from each other; were away from other trees and faced different directions. The plots were marked out using bamboo canes and a tape measure. Then either 20 or 30 randomly chosen 1 m<sup>2</sup> square quadrats were surveyed within the rectangular plot. The cumulative length of dropping in a quadrat was measured to the nearest centimeter with a ruler.</p> <p>The results are found in file DroppingsMeasurements.csv.</p> <p>The columns of this file are as follows</p> <p>Plot - The identifying number given to the plot</p> <p>X - The distance parallel to the woodland-lawn boundary</p> <p>Y - The distance from the woodland-lawn boundary</p> <p>Length - The total length in centimeters of the dropping found in a 1m<sup>2</sup> quadrat</p> <p>Prunella - coverage of <em>Prunella vulgaris</em> L. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Renoncule - coverage of <em>Ranunculus</em> sp. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Bellis - coverage of <em>Bellis perennis</em> L. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Lotus - coverage of <em>Lotus</em> sp. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Glechoma - coverage of <em>Glechoma hederacea</em> L. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Four species of geese are present in the Botanic Garden and may have contributed droppings to the observations. These species are <em>Alopochen aegyptiaca</em> (L. 1766) (Egyptian geese), <em>Branta canadensis</em> (L. 1758) (Canada geese), <em>Anser anser</em> (L. 1758) (greylag geese) and <em>Branta leucopsis</em> (Bechstein, 1803) (barnacle geese).</p>
Dataset for "Quantification of Muscle Fiber Malformations Using Edge Detection to Investigate Chronic Wound Healing"
<p>Primary images, spreadsheets and files for "Quantification of Muscle Fiber Malformations Using Edge Detection to Investigate Chronic Wound Healing"</p>
The cell adhesion molecule Sdk1 shapes assembly of a retinal circuit that detects localized edges
<p>Nearly 50 different mouse retinal ganglion cell (RGC) types sample the visual scene for distinct features. RGC feature selectivity arises from its synapses with a specific subset of amacrine (AC) and bipolar cell (BC) types, but how RGC dendrites arborize and collect input from these specific subsets remains poorly understood. Here we examine the hypothesis that RGCs employ molecular recognition systems to meet this challenge. By combining calcium imaging and type-specific histological stains we define a family of circuits that express the recognition molecule Sidekick 1 (Sdk1) which include a novel RGC type (S1-RGC) that responds to local edges. Genetic and physiological studies revealed that Sdk1 loss selectively disrupts S1-RGC visual responses which result from a loss of excitatory and inhibitory inputs and selective dendritic deficits on this neuron. We conclude that Sdk1 shapes dendrite growth and wiring to help S1-RGCs become feature selective.</p>
In Search of the Edge: A Bayesian Exploration of the Detectability of Red Edges in Exoplanet Reflection Spectra
<p>This repository contains surface albedos for a paper submitted to AAS journals under the same title. Here we include a a realistic Earth-like surface albedo and the raw albedo files used for it's calculation.</p>
Early Detection of GEnetic Risk (EDGE)
ClinicalTrials.gov study NCT04746794. IPD Sharing: NO. Countries: 1. Publications: 1.
The cell adhesion molecule Sdk1 shapes assembly of a retinal circuit that detects localized edges
Open the record for dataset details and reuse information.
Ultra-low-power Edge Detection Based on Ferroelectric Field Effect Transistor (data and code)
Open the record for dataset details and reuse information.
A Multimodal Dataset for Automatic Edge-AI Cough Detection
<p>Counting the number of times a patient coughs per day is an essential biomarker in determining treatment efficacy for novel antitussive therapies and personalizing patient care. There is a need for wearable devices that employ multimodal sensors to perform accurate, privacy-preserving, automatic cough counting algorithms directly on the device in an edge-AI fashion. To advance this research field, we contribute the first publicly accessible cough counting dataset of multimodal biosignals. The database contains nearly 4 hours of biosignal data, with both acoustic and kinematic modalities, covering 4,300 annotated cough events. Furthermore, several non-cough sounds (i.e. breathing, laughing, and throat clearing), background noises (i.e. music, traffic, bystander coughing) and motion scenarios (i.e. sitting, walking) mimicking daily life activities are also present, which the research community can use to accelerate ML algorithm development.</p> <p>For detailed information about using this dataset to train edge-AI models and example code, please refer to our public Git repository: https://github.com/esl-epfl/edge-ai-cough-count/</p>
CASENet: Deep Category-Aware Semantic Edge Detection
<p><strong>Introduction</strong></p> <p>This dataset includes initial and trained model weights (caffemodel files) for SBD/Cityscapes datasets which is used in our CASENet software available (<a href="https://github.com/merlresearch/CASENet">https://github.com/merlresearch/CASENet</a>).</p> <p><strong>Citation</strong></p> <p>If you use the data, please cite the following <a href="https://www.merl.com/publications/TR2017-100">TR2017-100</a>:</p> <pre><code>@inproceedings{Yu2017jul, author = {Yu, Zhiding and Feng, Chen and Liu, Ming-Yu and Ramalingam, Srikumar}, title = {CASENet: Deep Category-Aware Semantic Edge Detection}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = 2017, month = jul, doi = {10.1109/CVPR.2017.191}, url = {https://www.merl.com/publications/TR2017-100} }</code></pre> <p>Copyright and License<br> The CASENet dataset is released under<a href="https://creativecommons.org/licenses/by-sa/4.0/"> CC-BY-SA-4.0 license</a>.</p> <p><br> All data:</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2017, 2023 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre> <p><br> </p>
Heidelberg Edge Perimetry (HEP) Detecting Glaucomatous Visual Field Defects
ClinicalTrials.gov study NCT02526654. IPD Sharing: NO. Countries: 0. Publications: 1.
Data and Sourcecode from: Neural Network-based Occupancy Detection on the Edge
<div> <h2>Environmental Data Collected for Data-Driven Occupancy Detection</h2> <h3>Version Information</h3> <p>v1.0: Holds repository alongside the dataset inside<br>v2.0: Holds dataset in a seperate zip folder</p> <h3>Dataset Information</h3> <div>The following data is collected from LoRa sensors of two rooms for a period of three months in an office building on the ground floor in Graz, Austria:</div> <div> <ul> <li>Open status of windows/doors</li> <li>Relative humidity</li> <li>CO2 concentration</li> <li>Ambient temperature</li> <li>PIR-based motion counter</li> <li>Light level</li> <li>IR-based occupancy (only room A)</li> <li>Average/peak sound level</li> <li>Radar-based people counter (left-to-right and right-to-left; only room A; <em><strong>no</strong> trustworthy ground truth!</em>)</li> </ul> </div> <div> </div> <div>Folder Organization in occupancy-detection-dataset.zip</div> <br> <div> ├── data</div> <div> │ ├── interim <- Intermediate data of room A and B that has been transformed.</div> <div> │ └── raw <- The original, immutable sensor data dump of room A and B.</div> <br><br> <div><strong>Raw Data</strong></div> <div>Raw sensor data of room A and B consisting of six and two work places respectively. Data is gathered in an interval of five minutes.</div> <br> <div>Note:</div> <ul> <li>Timezone ist UTC+00:00.</li> <li>Column "occupancy" in df_features.csv refers to IR based occupancy sensor from Elsys ERS Eye (Possible values 0-2).</li> <li>Column "motion" in df_features.csv refers to a PIR based motion counter.</li> <li>IR-based occupancy is not measured in room B.</li> </ul> <br> <div><strong>Intermediate Data</strong></div> <div>Event-based (door and window sensors) and interval based (humidity, CO2, temperature, ....) data is synchronized to retrieve a homogenous data set.</div> <div>Window columns are merged to represent the number of open windows. Nothing else was applied to the data.</div> <br> <div><strong>Ground Truth</strong></div> <div>Image-based occupancy ground truth data is separated in a file (df_occ.csv).</div> <div>It describes the number of occupants at a certain time stamp provided from images (manually labelled).</div> <h3>References</h3> <p>Coming soon.</p> </div>
Advancing Lung Cancer Screening: Artificial Intelligence, Multimodal Imaging and Cutting-Edge Technologies for Early Detection and Characterization
ClinicalTrials.gov study NCT06531343. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Detection and tracking of turbulent structures in the edge of tokamak plasmas using machine learning
<p>This repository contains the data used in the study entitled "Detection and tracking of turbulent structures in the edge of tokamak plasmas: use of an ultra-fast camera and comparison of machine learning and Kalman filter methods". The data was collected using an ultra-fast camera on the COMPASS device.</p> <p><strong><em>This repository includes:</em></strong></p> <p><strong>Fast passive imaging data:</strong> High-frequency captures of turbulent structures present in the edge of tokamak plasmas.<br><strong>Labels :</strong> Annotation of turbulent structures for training and validation of detection and tracking algorithms.<br><strong>Detection and tracking results using YOLO:</strong> Outputs from the YOLO (You Only Look Once) model applied to turbulent data.<br><br><strong><em>Objective</em></strong><br>This dataset is intended to provide a complete and reproducible set for research into the detection and tracking of turbulent structures in tokamak plasmas. It can be used to compare the performance of machine learning methods with traditional techniques, and to encourage further research in this crucial area for plasma physics and nuclear fusion.</p> <p><br>Researchers are encouraged to use these data to:</p> <p>Replicate the results of the original study.<br>Develop and test new techniques for detecting and tracking turbulent structures.Compare the performance of machine learning algorithms with conventional methods.</p> <p><br>References Please cite the repository as follows: S. Chouchene et al., (2024). Detection and tracking of turbulent structures in the edge of tokamak plasmas using machine learning. Zenodo. https://doi.org/10.5281/zenodo.12608068</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.