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38 results for “Line Detection”
Three Annotated Anomaly Detection Datasets for Line-Scan Algorithms
<h1>Summary</h1> <p>This dataset contains two hyperspectral and one multispectral anomaly detection images, and their corresponding binary pixel masks. They were initially used for real-time anomaly detection in line-scanning, but they can be used for any anomaly detection task.</p> <p>They are in .npy file format (will add tiff or geotiff variants in the future), with the image datasets being in the order of (height, width, channels). The SNP dataset was collected using sentinelhub, and the Synthetic dataset was collected from AVIRIS. The Python code used to analyse these datasets can be found at: https://github.com/WiseGamgee/HyperAD</p> <h1>How to Get Started</h1> <p>All that is needed to load these datasets is Python (preferably 3.8+) and the NumPy package. Example code for loading the Beach Dataset if you put it in a folder called "data" with the python script is:</p> <pre><code>import numpy as np # Load image file hsi_array = np.load("data/beach_hsi.npy") n_pixels, n_lines, n_bands = hsi_array.shape print(f"This dataset has {n_pixels} pixels, {n_lines} lines, and {n_bands}.") # Load image mask mask_array = np.load("data/beach_mask.npy") m_pixels, m_lines = mask_array.shape print(f"The corresponding anomaly mask is {m_pixels} pixels by {m_lines} lines.")</code></pre> <h1>Citing the Datasets</h1> <p>If you use any of these datasets, please cite the following paper:</p> <pre><code>@article{garske2024erx,</code><br><code> title={ERX - a Fast Real-Time Anomaly Detection Algorithm for Hyperspectral Line-Scanning},</code><br><code> author={Garske, Samuel and Evans, Bradley and Artlett, Christopher and Wong, KC},</code><br><code> journal={arXiv preprint arXiv:2408.14947},</code><br><code> year={2024},</code><br><code>}</code></pre> <div> <pre>If you use the beach dataset please cite the following paper as well (original source):</pre> </div> <pre><code>@article{mao2022openhsi, title={OpenHSI: A complete open-source hyperspectral imaging solution for everyone}, author={Mao, Yiwei and Betters, Christopher H and Evans, Bradley and Artlett, Christopher P and Leon-Saval, Sergio G and Garske, Samuel and Cairns, Iver H and Cocks, Terry and Winter, Robert and Dell, Timothy}, journal={Remote Sensing}, volume={14}, number={9}, pages={2244}, year={2022}, publisher={MDPI} }</code></pre>
Raw and processed cell lines (melanomaC818/melanomaMUM-2B/SK-MEL-28) data for detecting DMKN's mutations in melanoma cancer
<p>Raw and processed cell lines (melanomaC818/melanomaMUM-2B/SK-MEL-28) data for detecting DMKN's mutations in melanoma cancer. This research was concluded that DMKN is a trigger of epithelial-mesenchymal transition-driven melanoma.</p>
Tango Spacecraft Wireframe Dataset Model for Line Segments Detection
<p><strong>Reference Paper:</strong></p> <p><a href="https://doi.org/10.1016/j.actaastro.2023.01.012"><strong>M. Bechini, M. Lavagna, P. Lunghi, Dataset generation and validation for spacecraft pose estimation via monocular images processing, Acta Astronautica 204 (2023) 358–369</strong></a></p> <p><a href="https://www.researchgate.net/publication/361924362_Spacecraft_Pose_Estimation_via_Monocular_Image_Processing_Dataset_Generation_and_Validation">M. Bechini, P. Lunghi, M. Lavagna. "Spacecraft Pose Estimation via Monocular Image Processing: Dataset Generation and Validation". In 9th European Conference for Aeronautics and Aerospace Sciences (EUCASS)</a></p> <p><strong>General Description:</strong></p> <p>The "<em>Tango Spacecraft Wireframe Dataset Model for Line Segments Detection</em>" dataset here published should be used for line detection and segmentation tasks. It is split into 30002 train images and 3002 test images representing the Tango spacecraft from Prisma mission, being the only publicly available dataset of synthetic space-borne images tailored to line detection tasks (up to our knowledge). The label of each image gives the reprojection of a simplified wireframe model of Tango on the image plane split into lines. The labels are written following the Wireframe Model format. The "<em>Tango Spacecraft Wireframe Dataset Model for Line Segments Detection</em>" is also the largest dataset with wireframe annotations available up to date. More information on the dataset split and on the label format are reported below. </p> <p><strong>Images Information:</strong></p> <p>The dataset comprises 30002 synthetic grayscale images of Tango spacecraft from Prisma mission that serves as train set, while the test set is formed by 3002 synthetic grayscale images of Tango spacecraft from Prisma mission in PNG format. About 1/6 of the images both in the train and in the test set have a non-black background, obtained by rendering an Earth-like model in the raytracing process used to define the images reported. The images are noise-free to increase the flexibility of the dataset. The illumination direction of the spacecraft in the scene is uniformly distributed in the 3D space in agreement with the Sun position constraints.</p> <p><br> <strong>Labels Information:</strong></p> <p>Labels in the Wireframe dataset format are here provided in separated JSON files. The files are formatted per each image as in the following example:</p> <ul> <li> width : 98 # width in pixels (int) of the current image</li> <li> height : 176 # height in pixels (int) of the current image</li> <li> lines : [[line1], [line2], ..., [lineN]] # list of lines in each image</li> <li> filename : tango_img_866.png # string with image name and format</li> </ul> <p>Per each line (line1, ... , lineN) in lines, the format is [x0, y0, x1, y1].</p> <p>(x0, y0) are the coordinates (float) of the line starting point in the image reference frame (x pointing right and y pointing down with origin located in the top-left corner of the image).<br> (X1, y1) are the coordinates (float) of the line ending point in the image reference frame (x pointing right and y pointing down with origin located in the top-left corner of the image).</p> <p>Note that the starting point is assumed to be the left-most endpoint (lower x coordinate in image reference frame) of each line. In the case of vertical lines, the starting point is the upper-most endpoint (lower y coordinate in image reference frame) of each line.</p> <p><strong>VERSION CONTROL</strong></p> <ul> <li><strong>v1.0</strong>: All the images (both for train and test) have different resolutions, with Tango always centered in the image. The height of the images is in the range 19 - 352 pixels, while the width is in the range 16 - 336 pixels. The height over width ratio spans from 0.34 to 3.25.</li> <li><strong>v2.0</strong>: This version contains all the images of v1.0 in the .zip folder named <em>Tango_WF.zip</em>, while in the .zip folder named <em>Tango_WF_fullscale.zip</em> there is the dataset (both train and test) of full scale images. These images have width=height=1024 pixels. The position of tango with respect to the camera is randomly selected from a uniform distribution, but it is ensured the full visibility in all the images. The labels for the wireframe are in the same format of v1.0.</li> </ul> <p>Note: the dataset in v1.0 is obtained by cropping the fullscale images in v2.0 and by properly rescaling the wireframe annotations.</p> <p>Note: this dataset contains the same images of the <em>"Tango Spacecraft Dataset for Region of Interest Estimation and Semantic Segmentation</em><em>"</em> v1.0 (DOI: <a href="https://doi.org/10.5281/zenodo.6507863">https://doi.org/10.5281/zenodo.6507863</a>) and also "<em>Tango Spacecraft Dataset for Monocular Pose Estimation</em>" v1.0 (DOI: <a href="https://doi.org/10.5281/zenodo.6499007">https://doi.org/10.5281/zenodo.6499007</a>) and they can be used together by combining the annotations of the relative pose and the ones of the reprojected wireframe model of Tango, with also the ones of the ROI. <strong>These three datasets give the most comprehensive dataset of space borne synthetic images ever published</strong> (up to our knowledge).</p>
Benchmarking Illumina RNA-seq fusion transcript detection methods - cancer cell lines RNA-seq
<p>Cancer cell line RNA-seq data (reads or names of reads from CCLE data) used for benchmarking Illumina-based fusion detection methods as used in:</p> <p>Haas, B.J., Dobin, A., Li, B. <em>et al.</em> Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods. <em>Genome Biol</em> <strong>20</strong>, 213 (2019). https://doi.org/10.1186/s13059-019-1842-9</p> <p> </p> <p>For CCLE data, direct sharing of fastq files was not possible. CCLE data must be obtained from:</p> <p> https://portals.broadinstitute.org/ccle/home</p> <p>Instead, the identifiers for the reads leveraged as part of our study are made available, and these reads can be extracted from the CCLE fastq files directly once obtained from the primary source.</p> <p><br>For the non-CCLE data, the exact reads leveraged by our study are made directly available here in fastq format.</p>
CO emission line spectra of the detected IRAM 30m CO-CAVITY galaxies.
<p>Figures show the observed spectra of the CO(1-0) and CO(2-1) emission lines of the CO-CAVITY galaxies.</p>
Dataset for Paper: Text Line Detection and Recognition of Greek Polytonic Documents
<p>Dataset for Paper: Text Line Detection and Recognition of Greek Polytonic Documents, P. Kaddas, B. Gatos, K. Palaiologos, K. Christopoulou and K. Kritsis, 4th Workshop on Machine Learning (WML), San Jose, California, USA</p> <p>We introduce a new dataset, named GTLD-small dataset, with annotated text line quadrilateral polygons of 1.642 documents, including annotations on 3 datasets (Tobacco-3482, PIOP and ShakeIT dataset)</p> <table> <caption>Overview of the datasets included in this work and the number of images used for training, validation and testing.</caption> <thead> <tr> <th scope="col">Collection</th> <th scope="col">#Total</th> <th scope="col">#train</th> <th scope="col">#val</th> <th scope="col">#test</th> </tr> </thead> <tbody> <tr> <td>PIOP-small</td> <td>950</td> <td>672</td> <td>90</td> <td>188</td> </tr> <tr> <td>ShakeIT-small</td> <td>357</td> <td>264</td> <td>27</td> <td>66</td> </tr> <tr> <td>Tobacco-3482-small</td> <td>335</td> <td>240</td> <td>30</td> <td>65</td> </tr> </tbody> </table> <p> </p>
A Line-level Explainable Vulnerability Detection Approach for Java
<p>Given our modern society’s level of dependency on IT technology, high quality and security are not just desirable but rather vital properties of current software systems. Empirical methods leveraging the available rich open-source data and advanced data processing techniques of ML algorithms can help software developers ensure these properties. Nonetheless, state-of-the-art bug and vulnerability prediction methods are rarely used in practice due to numerous reasons. The predictions are not actionable in most of the cases due to their level of granularity (i.e., they mark entire classes/files to be buggy or vulnerable) and because the methods seldom provide explanation why a fragment of source code is problematic. In this paper, we present a novel Java vulnerability detection method that addresses both of these issues. It is an adaptation of our previous method for JavaScript that is capable of pinpointing vulnerable source code lines of a program together with a prototype-based explanation. The method relies on the word2vec similarity of code fragments to known vulnerable source code lines. Our empirical evaluation showed promising results, we could detect 61% and 41% of the vulnerable code lines by flagging only 43% and 22% of the program code lines, respectively, using two of the best detection configurations.</p> <p>The dataset contains the extracted vulnerable code lines and word2vec models used in the experiment on 205 Java projects.</p>
Automatic unsupported electromembrane extraction followed by on-line electrochemical detection of diclofenac in urine
<p>Raw voltammograms (including calibration) of data presented in Table 1 of the article entitled "Programmable millifluidic platform integrating automatic electromembrane extraction clean-up and in-line electrochemical detection: A proof of concept"<strong> </strong>published in ACS Sensors (<a href="https://doi.org/10.1021/acssensors.2c01648">10.1021/acssensors.2c01648</a>)</p>
ALMA detection of CO rotational line emission in red supergiant stars of the massive young star cluster RSGC1
<p>Input files and data for the MESA simulations in the paper "ALMA detection of CO rotational line emission in red supergiant stars of the massive young star cluster RSGC1". Runs where performed with version r23.05.1 of the MESA code using the MESA SDK version x86_64-linux-22.6.1. Folder named "Decin" contains the simulation with the new mass loss rate described in the paper, while the folder named NdJ contains the input files for the simulation performed with the Nieuwenhuijzen & de Jager rates.</p>
Evaluation of PreveCol's Efficiency As a Second Line Method for the Early Colorectal Cancer Detection
ClinicalTrials.gov study NCT06703632. IPD Sharing: NO. Countries: 2. Publications: 4.
Plasma microRNA Profiling as First Line Screening Test for Lung Cancer Detection: a Prospective Study
ClinicalTrials.gov study NCT02247453. IPD Sharing: UNDECIDED. Countries: 1. Publications: 12.
Interest of the F18-choline as a Second Line of the Tracer for Detection of Parathyroid Adenomas
ClinicalTrials.gov study NCT02432599. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Study for Early Detection of Drug Interactions in Older Hospitalized Patients Using on Line Software
ClinicalTrials.gov study NCT00850330. IPD Sharing: Not stated. Countries: 1. Publications: 17.
Drone Flight Videos for Vegetation Monitoring, Fire Sensor Detection, and Power Line Inspection
<p>This video set showcases drone flights conducted in three different scenarios: vegetation monitoring in natural areas, recognition and localization of a fire detection sensor, and visual inspection of a power line. The videos serve as practical examples of how drones can be used in environmental monitoring and critical infrastructure maintenance applications. The visualization of the results and analysis of the collected data is performed through the GridWatch platform, enabling efficient and detailed monitoring. </p>
Dataset of Annotated Virtual Detection Line for Road Traffic Monitoring
<p>Annotated Virtual Detection Line (AVDL) dataset is presented for multiple object detection, consisting of 74 108 data files and 74 108 manually annotated files divided into six classes: Vehicles, Trucks, Pedestrians, Bicycles, Motorcycles, and Scooters from the video. The data were captured from real road scenes using 50 video cameras from the leading video camera manufacturers at different road locations and under different meteorological conditions. The AVDL dataset consists of two directories, the Data directory and the Labels directory. Both directories provide the data as NumPy arrays. The dataset can be used to train and test deep neural network models for traffic and pedestrian detection, recognition, and counting.</p>
Detection of new pathways involved in the acceptance and the utilization of plant-based diet in isogenic lines of rainbow trout fry
GEO Series GSE92673. Oncorhynchus mykiss. 34 samples. Type: Expression profiling by array.
Transcriptome analysis of four maize lines to detect the different responses to chilling stress and reveal the survival mechanisms in maize
GEO Series GSE167156. Zea mays. 16 samples. Type: Expression profiling by high throughput sequencing.
To detect change of gene expression after knock-down of hormone receptor in hepatocellular carcinoma cell line
GEO Series GSE65081. Homo sapiens. 4 samples. Type: Expression profiling by array; Non-coding RNA profiling by array.
Video results of the study "Dynamic Region of Interest Generation for Maritime Horizon Line Detection using Time Series Analysis"
<p># Video Results of the Study "Dynamic Region of Interest Generation for Maritime Horizon Line Detection using Time Series Analysis"</p> <p>This repository contains the result videos from the study titled *"Dynamic Region of Interest Generation for Maritime Horizon Line Detection using Time Series Analysis"*. These videos showcase the performance of the algorithm on two datasets: the **Singapore Maritime Dataset (Onboard Segment)** and the **Buoy Dataset**. Please note that the original datasets are not included in this repository but are publicly available elsewhere.</p> <p>## Contents</p> <p>### 1. Singapore Maritime Dataset (Onboard Segment)<br>The videos labeled with **MVI** belong to the Singapore Maritime Dataset. These result videos demonstrate the performance of the horizon line detection algorithm on onboard maritime footage. For example:<br>- **MVI_0792_VIS_OB.avi** - Original input video (available in the Singapore Maritime Dataset)<br>- **MVI_0792_VIS_OB_R.avi** - The output video showing the results of the algorithm</p> <p>### 2. Buoy Dataset<br>The videos labeled with **buoyGT** belong to the Buoy Dataset. These results show the performance of the algorithm near maritime buoys. For example:<br>- **buoyGT_2_5_3_0.avi** - Original input video (available in the Buoy Dataset)<br>- **buoyGT_2_5_3_0_R.avi** - The output video showing the results of the algorithm</p> <p>## Video Naming Convention<br>Each result video follows a consistent naming convention related to the original input videos:<br>- **Original Video Filename**: [dataset]_[video details].avi<br>- **Result Video Filename**: [original video filename]_R.avi</p> <p>For instance:<br>- **buoyGT_2_5_3_0.avi** corresponds to **buoyGT_2_5_3_0_R.avi**<br>- **MVI_0792_VIS_OB.avi** corresponds to **MVI_0792_VIS_OB_R.avi**</p> <p>The **_R** suffix in the result videos indicates that the video contains the output of the horizon line detection algorithm.</p> <p>## How to Use<br>- To view the results for a specific video, locate the original input video in the corresponding public dataset and find the matching result video in this repository.<br>- For example, the result for the video "buoyGT_2_5_3_0.avi" can be found as "buoyGT_2_5_3_0_R.avi".</p> <p>## Datasets<br>The original datasets used in this study are not included in this repository. They are publicly available from the following sources:<br>1. **Singapore Maritime Dataset** (Onboard Segment)<br>2. **Buoy Dataset**</p> <p>Please refer to the respective dataset repositories for the original video files.</p> <p>## Citation<br>If you use these videos or the method presented in this study in your work, please cite the following:</p> <p>*Dynamic Region of Interest Generation for Maritime Horizon Line Detection using Time Series Analysis*.</p>
Home-based online line bisection test detects visuo-spatial neglect and pseudoneglect in Parkinson's Disease
<p>Acquired data and analysis codes of the paper: </p> <p>Albert, Louis, et al. "Home-based online line bisection test detects visuo-spatial neglect and pseudoneglect in Parkinson's disease." <em>Parkinsonism & related disorders</em> 130 (2025): 107195.</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.