Image Fusion
The presented dataset is a supplementary material to the paper [1] and it represents the X-Ray Energy Dispersive (EDS)/ Scanning Electron Microscopy (SEM) images of a shungite-mineral particle. Pansharpening is a procedure for enhancing the spatial resolution of a multispectral image, here the EDS individual bands, with a high-spatial panchromatic image, here the SEM image. Pansharpening techniques are usually tested with remote sensed data, but the procedures have been efficient in close-range MS-PAN pairs as well [3].
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Please cite the following paper when using this dataset:
N. Thakur, "Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets from 2017–2022 and 100 Research Questions", Journal of Analytics, Volume 1, Issue 2, 2022, pp. 72-97, DOI: https://doi.org/10.3390/analytics1020007
Abstract
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Sequential skeleton and average foot pressure data for normal and five pathological gaits (i.e., antalgic, lurching, steppage, stiff-legged, and Trendelenburg) were simultaneously collected. The skeleton data were collected by using Azure Kinect (Microsoft Corp. Redmond, WA, USA). The average foot pressure data were collected by GW1100 (GHIWell, Korea). 12 healthy subjects participated in data collection. They simulated the pathological gaits under strict supervision. A total of 1,440 data instances (12 people x 6 gait types x 20 walkings) were collected.
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There is an industry gap for publicly available electric utility infrastructure imagery. The Electric Power Research Institute (EPRI) is filling this gap to support public and private sector AI innovation. This dataset consists of ~30,000 images of overhead Distribution infrastructure. These images have been anonymized, reviewed, and .exif image-data scrubbed. EPRI intends to label these data to support its own research activities. As these labels are created, EPRI will periodically update this dataset with those data.
Update: July 2022
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We present here an annotated thermal dataset which is linked to the dataset present in https://ieee-dataport.org/open-access/thermal-visual-paired-dataset
To our knowledge, this is the only public dataset at present, which has multi class annotation on thermal images, comprised of 5 different classes.
This database was hand annotated over a period of 130 work hours.
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