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11 results for “Object Classification”
VCM Dataset for the Classification of Resident Space Objects
<p>The Vector Covariance Message (VCM) data comprise 22,303 RSOs over a period of six months (9/1/2022-2/28/2023). VCM data consist of Resident Space Objects (RSOs) ephemerides from a high-precision special perturbations orbit propagator and estimator using tracking observations. VCMs are issued by the US Space Force (USSF) Space Command (USSPACECOM) and were provided through an Orbital Data Request (ODR) the authors submitted to the 18th Space Defense Squadron (18th SDS). </p> <p>The dataset is organized into subfolders, each containing VCMs for a specific satellite. Filenames correspond to the satellite's NORAD ID (North American Aerospace Defense Catalog Number). A readme file provides details about the VCM content and format. Note that the full covariance matrix has been excluded for public release, whereas the standard deviation of error in satellite's position and velocity is provided.</p> <p>The VCM data have been used in the following work, "Early Classification of Space Objects based on Astrometric Time Series Data", presented at the 25th Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS) in Maui, Hawaii, United States.</p>
Spike-timing based coding in neuromimetic tactile system enables dynamic object classification
<p>Coding dynamic tactile information in spike timing is essential to human haptic exploration and dexterous object manipulation. Conventional electronic skins generate frames of tactile signals upon interaction with objects and are unfortunately ill-suited for efficient coding of temporal information and rapid feature extraction. Here, we report a neuromorphic tactile system that uses spike timing, especially the first-spike timing, to code dynamic tactile information about touch and grasp. This strategy enables the system to seamlessly code highly dynamic information with millisecond temporal resolution on par with the biological nervous system, yielding dynamic extraction of tactile features. Upon interaction with objects, the system rapidly classifies them in the initial phase of touch and grasp, thus paving the way to fast tactile feedback desired for neuro-robotics and neuro-prosthetics.</p>
Sim2Real Bilevel Adaptation for Object Surface Classification using Vision-Based Tactile Sensors, dataset
<p>This is the dataset used in the 'Sim2Real Bilevel Adaptation for Object Surface Classification using Vision-Based Tactile Sensors.' It contains 2 folders:</p> <ol> <li><strong>train_diffusion</strong>: a set of 5,000 images used to train the diffusion model.</li> <li><strong>translated</strong>: a set of approximately 50,000 images converted via a diffusion model pre-trained on a small set of real images.</li> </ol> <p>The 'translated' folder contains a <code>labels.csv</code> file, providing the corresponding label for every image.</p>
Data from: Integrating a UAV-derived DEM in object-based image analysis increases habitat classification accuracy on coral reefs
<p>Very shallow coral reefs (< 5 m deep) are naturally exposed to strong sea surface temperature variations, UV radiation and other stressors exacerbated by climate change, raising great concern over their future. As such, accurate and ecologically informative coral reef maps are fundamental for their management and conservation. Since traditional mapping and monitoring methods fall short in very shallow habitats, shallow reefs are increasingly mapped with Unmanned Aerial Vehicles (UAVs). UAV-imagery is commonly processed with Structure-from-Motion (SfM) to create orthomosaics and Digital Elevation Models (DEMs) spanning several hundred metres. Techniques to convert these SfM products to ecologically relevant habitat maps are still relatively underdeveloped. Here we demonstrate that incorporating geomorphometric variables (the DEM and its derivatives) in addition to spectral information (the orthomosaic) can greatly enhance the accuracy of automatic habitat classification. Therefore, we mapped three very shallow reef areas off KAUST on the Saudi Arabian Red Sea coast with an RTK-ready UAV. Imagery was processed with SfM, and classified through Object-Based Image Analysis (OBIA). Within our OBIA workflow, we observed overall accuracy increases of up to 11% when training a Random Forest classifier on both spectral and geomorphometric variables as opposed to traditional methods that only use spectral information. Our work highlights the potential of incorporating a UAV's DEM in OBIA for benthic habitat mapping, a promising but still scarcely exploited asset.</p>
Data from: Integrating a UAV-derived DEM in object-based image analysis increases habitat classification accuracy on coral reefs
Open the record for dataset details and reuse information.
Spike-timing based coding in neuromimetic tactile system enables dynamic object classification
Open the record for dataset details and reuse information.
Comparison and assessment of different object-based classifications using machine learning algorithms and UAVs multispectral imagery in the framework of precision agriculture
<p>Supplementary material of the paper</p>
Landsat 8 Scene Classification Map for a minimal working example for spatio-temporal object management
<p>Landsat 8 Scene Classification Map for a minimal working example for spatio-temporal object management</p>
Prospective Development and Validation of an Objective Classification for Difficult Facemask Ventilation: the MASCAN Score
ClinicalTrials.gov study NCT07275502. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Inflammation, adenoma and cancer: objective classification of colon biopsy specimens with gene expression signature
GEO Series GSE4183. Homo sapiens. 53 samples. Type: Expression profiling by array.
Smaller is better? Unduly nice accuracy assessments in image classification due to spatial autocorrelation in identification of small sized objects
<p>Deriving the thematic accuracy of models is a fundamental part of image classification analyses. However, due to high spatial autocorrelation in remotely sensed imagery, accuracy assessments can be biased, which leads to relevant overestimation of accuracies. </p>
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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.