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11 results for “bounding box”
Bounding box coordinates of von Karman vortex street
<p>Those files contain bounding box coordinates of von Karman vortex street annotated by VoTT. The von Karman vortex street is annotated as one object in vortex_street.tar.gz, while each vortex in the von Karman vortex street is annotated as one object in vortices.tar.gz. The original video file is from https://doi.org/10.1063/1.4921683.1 to 1.4921683.9. See also the reference.</p>
Cholec80-Boxes: Bounding-Box Labels for Surgical Tools in Five Cholecystectomy Videos
<p> </p> <p>The dataset is descriped in a pending publication titled "Cholec80-Boxes: Bounding-box Labeling Data for Surgical Tools in Cholecystectomy Images". The dataset was used in the following studies titled:</p> <ul> <li>"Surgical tool classification & localisation using attention and multi-feature fusion deep learning approach".</li> <li>"Laparoscopic video analysis using temporal, attention, and multi-feature fusion based-approaches".</li> <li>"Analysing attention convolutional neural network for surgical tool localisation: A feasibility study".</li> </ul> <p>The dataset consists of cholecystectomy images and bounding-box labels for surgical tools. These images were extracted from five videos of the Cholec80 dataset (Twinanda et al., 2016) at a rate of 1 Hz. The images are stored in '.png' format with a resolution of 854*480 pixels. Each video’s images are organized in a separate folder. The labeling data are stored in a CSV file, which contains the region of interest (ROI) labels for each surgical tool visible in the extracted images. Additionally, the CSV file provides information about each labeled image. Table 1 presents a content description of the 'ROI_Labels.csv' file.</p> <p><strong>Table 1:</strong> Description of 'ROI_Labels.csv' file.</p> <table> <tbody> <tr> <td><strong>Column Name</strong></td> <td><strong>Description</strong></td> <td><strong>Type</strong></td> </tr> <tr> <td><em>Surgery_num</em></td> <td>Procedure number in the Cholec80 dataset from which the image was extracted.</td> <td>Integer</td> </tr> <tr> <td><em>Dir</em></td> <td>Directory of the image folder.</td> <td>String</td> </tr> <tr> <td><em>FrameName</em></td> <td>Image name in the format '<em>Video_SS_fffff.png', </em>where <em>SS is the Surgery_num and fffff is the frame number in the video.</em></td> <td>String</td> </tr> <tr> <td><em>NumBBox_inFrame</em></td> <td>The bounding-box number in the image.</td> <td>Integer</td> </tr> <tr> <td><em>ToolName</em></td> <td>Name of the surgical tool.</td> <td>String</td> </tr> <tr> <td><em>BBox</em>_<em>X</em></td> <td>X-coordinate of the top-left corner.</td> <td>Integer</td> </tr> <tr> <td><em>BBox_Y</em></td> <td>Y-coordinate of the top-left corner.</td> <td>Integer</td> </tr> <tr> <td><em>BBox_Width</em></td> <td>Bounding box width.</td> <td>Integer</td> </tr> <tr> <td><em>BBox_Height</em></td> <td>Bounding box height.</td> <td>Integer</td> </tr> </tbody> </table> <p> </p> <p><strong>Citing This Dataset:</strong></p> <p>When using this dataset, please cite the following publications:</p> <ul> <li>Jalal, N. A., Alshirbaji, T. A., Docherty, P. D., Arabian, H., Laufer, B., Krueger-Ziolek, S., Neumuth, T. & Moeller, K. (2023). Laparoscopic video analysis using temporal, attention, and multi-feature fusion based-approaches. <em>Sensors</em>, <em>23</em>(4), 1958.<br><br></li> <li>Jalal, N. A., Alshirbaji, T. A., Docherty, P. D., Arabian, H., Neumuth, T., & Möller, K. (2023). Surgical tool classification & localisation using attention and multi-feature fusion deep learning approach. IFAC-PapersOnLine, 56(2), 5626-5631.</li> <li> <p>Abdulbaki Alshirbaji, T., Arabian, H., Jalal, N. A., Battistel, A., Docherty, P. D., Neumuth, T., & Moeller, K. Cholec80-Boxes: Bounding-box labeling data for surgical tools in cholecystectomy images. (<em>to be submitted</em>). </p> </li> <li>Twinanda, A. P., Shehata, S., Mutter, D., Marescaux, J., De Mathelin, M., & Padoy, N. (2016). Endonet: a deep architecture for recognition tasks on laparoscopic videos. <em>IEEE transactions on medical imaging</em>, <em>36</em>(1), 86-97.</li> </ul>
Brightfield images of cells and spheroids in wells annotated with bounding boxes
<p>The images in this dataset show cells in different developmental stages upon forming spheroids. They can go through several developmental sages: Starting from cells, they turninto compacted objects and then into spheroids. Ultimately, they can die and disintegrate. The objects are annotated with bounding boxes that carry these respective labels. The dataset in its current form can be upload to an OMERO server using the omero-cli-transfer package - simply download the zip file, log into your omero server and use the `omero transfer unpack` command as shown on the <a href="https://github.com/ome/omero-cli-transfer">omero-cli-transfer documentation</a>.</p> <p>The dataset can be used to train object detection models such as a <a href="https://docs.ultralytics.com/models/yolov8/">yolo classifier </a>- the linked repository provides a tutorial on how to do so.</p>
Pockmark Bounding Box Detection and Segmentation Labels
<p>This dataset contains 256x256 pixel jpeg images of gridded depth values as well as binary masks for pixels containing pockmarks (these jpegs are merged together with the depth image on the left and the mask on the right, making a 512x256 image). These are contained within the subdirectory 'PockmarkMaskAnnotations'.</p> <p>Additionally, this dataset contains a csv containing bounding box annotations (label, bounding box coordinates in terms of image pixels, and a unique integer for the label) of pockmarks in each image. These are contained within the subdirectory 'PockmarkBoxAnnotations'.</p> <p>Together, these annotations can be used to construct either a bounding box object detector, a bounding box and mask object detector, or a semantic segmentation model.</p> <p>These were the labels used for the experiments described in Lundine et al., 2023. See this reference to find original data sources to the collected bathymetry data.</p> <p>Lundine, M., Brothers, L., Trembanis, A., Deep learning-based pockmark detection: implications for quantitative seafloor characterization, Geomorphology, 2023, Volume 421, 108524, <a href="https://doi.org/10.1016/j.geomorph.2022.108524">https://doi.org/10.1016/j.geomorph.2022.108524</a>.</p> <p> </p>
bioRxiv 10k figure bounding boxes
<p>This dataset contains figure bounding boxes corresponding to the <a href="https://doi.org/10.5281/zenodo.3873702">bioRxiv 10k dataset</a>.</p> <p>It provides annotations in two formats:</p> <ul> <li><a href="https://cocodataset.org/#format-data">COCO format (JSON)</a></li> <li>JATS XML with <a href="https://github.com/kermitt2/grobid/blob/0.7.0/doc/Coordinates-in-PDF.md">GROBID's "coords" attribute</a></li> </ul> <p>The COCO format contains bounding boxes in rendered pixel units, as well as PDF user units. The latter uses field names with the "pt_" prefix.</p> <p>The "coords" attribute uses the PDF user units.</p> <p>The dataset was generated by using an algorithm to find the figure images within the rendered PDF pages. The main algorithm used for that purpose is <a href="https://en.wikipedia.org/wiki/Scale-invariant_feature_transform">SIFT</a>. As a fallback, <a href="https://docs.opencv.org/4.5.3/d4/dc6/tutorial_py_template_matching.html">OpenCV's Template Matching</a> (with multi scaling) was used. There may be some error cases in the document. Very few documents were excluded, were neither algorithm was able to find any match for one of the figure images (six documents in the train subset, two documents in the test subset).</p> <p>Figure images may appear next to a figure description, but they may also appear as "attachments". The latter usually appears at the end of the document (but not always) and often on pages with dimensions different to the regular page size (but not always).</p> <p>This dataset itself doesn't contain any images. The PDF to render pages can be found in the <a href="https://doi.org/10.5281/zenodo.3873702">bioRxiv 10k dataset</a>.</p> <p>The dataset is intended for training or evaluation purposes of the semantic Figure extraction. The evaluation score would be calculated by comparing the extracted bounding boxes with the one from this purpose. (example implementation <a href="https://github.com/elifesciences/sciencebeam-judge">ScienceBeam Judge</a>)</p> <p>The dataset was created as part of <a href="https://elifesciences.org/">eLife</a>'s <a href="https://github.com/elifesciences/sciencebeam">ScienceBeam</a> project.</p>
Used portable batteries with bounding boxes
<p>This dataset contains images and labels of used portable batteries. Battery types are:</p> <ol> <li>Lithium-ion (LIION, class 0) </li> <li>Lithium polymer (LIPO, class 1) </li> <li>Lead-acid (PB, class 2)</li> <li>Nickel-cadmium (NICD, class 3)</li> <li>Nickel-metal hydride (NIMH, class 4)</li> </ol> <p>Each image contains multiple batteries of the same type. The file name defines the type (e.g. LIPO_IMG_4920.JPG is an image with multiple lithium polymer batteries). Each image file is accompanied by a label file (*.txt) with identical name except the file name extension. Each row in the label file contains a class label (0 to 4) followed by bounding box coordinates in YOLO format.</p> <p>This dataset was produced in Horizon 2020 funded project <a href="https://trinityrobotics.eu">TRINITY</a>.</p>
An endothelial monolayer with leukocytes annotated for classification with bounding boxes for YOLO
<p>Movies of leukocyte TEM generated by Max Grönloh (Sanquin Research) were used as input for deep learning analysis with YOLO on the ZeroCostDL4Mic platform.</p> <p>Bounding boxes generated with Makesense.ai by Guusje Mouton.</p> <p>After training, an unseen dataset was analysed. For results see: 'Filmpje_Predictions_YOLO.gif'</p> <p>The result shows a large number of false negatives, which can potentially be improved by increasing the volume of training data.</p>
Bounding-box detection data for delphinid whistles
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Object bounding box annotations for the GTEA Gaze+ dataset
<p>Object bounding box annotations for the GTEA Gaze+ dataset of the works <em>Learning to recognize daily actions using gaze</em> (Fathi et al., 2012) and <em>Delving into Egocentric Actions </em>(Li et al., 2015). The dataset contains folders for each of the subjects, within each of them folders for actions, and, within each action folder, a folder for each video. The video folder has a name composed of <name of the original video>_<start frame>_<end frame>. Within this folder, a json file for some frames can be found.</p> <p>The json contains two keys: <em>filename</em> and <em>objects</em>. <em>filename</em> refers to the path to the image and <em>objects</em> to a dictionary of objects. The keys of the dictionary are the objects present in the image. Each of the objects values is a list containing bounding box coordinates. Each coordinate list is composed of the ymin, xmin, ymax and ymax values.</p>
Optimizing Within-Distance Queries by Approximating Shapes with Maximal Bounded Boxes - Datasets
<p>Csv and sql files for the underlying data.<br> Figure 9: Calculating the distance between various number and type of shapes, i.e. from polygons, rotated and axis aligned rectangles, to points, polygons, rotated and axis aligned rectangles.<br> Table 1: Calculating distance between shapes of various complexity, i.e. points, lines, quadrilaterals, hexagons, dodecagons, icosagons, pentacontagons.<br> Table 2: Comparing the ordering of the shapes when distance operation is calculated, (a) from polygons to rectangles, (b) from rectangles to polygons.<br> Table 5 & 6: Within-distance and distance queries between (a) actual polygons, (b) their bounded rectangles, (c) actual polygons and points, (d) their bounded rectangles and points.</p>
FIGURE 1. The bounding-box method for measuring L and H in Reconciling diagnostic traits in living and fossil taxa: The taxonomy and evolution of the genus Microceratina (Crustacea, Ostracoda, Cytheruridae)
FIGURE 1. The bounding-box method for measuring L and H of valves in lateral position. Specimen as in Fig. 3A.
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.