This document describes the details of the BON Egocentric vision dataset. BON denotes the initials of the locations where the dataset was collected; Barcelona (Spain); Oxford (UK); and Nairobi (Kenya). BON comprises first-person video, recorded when subjects were conducting common office activities. The preceding version of this dataset, FPV-O dataset has fewersubjects for only a single location (Barcelona). To develop a location agnostic framework, data from multiple locations and/or office settings is essential.
Instructions are available on the attached document
The University of Turin (UniTO) released the open-access dataset Stoke collected for the homonymous Use Case 3 in the DeepHealth project (https://deephealth-project.eu/). UniToBrain is a dataset of Computed Tomography (CT) perfusion images (CTP).
Visit https://github.com/EIDOSlab/UC3-UNITOBrain to have a full companion code where a U-Net model is trained over the dataset.
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.
The annotation is done using the VGG Image Annotator (VIA) [Dutta, Abhishek, Ankush Gupta, and Andrew Zissermann. "VGG image annotator (VIA)." URL: http://www.robots.ox.ac.uk/~vgg/software/via (2016).].
We use the standard annotation format provided.
'sonel_annotation.csv' uses the image present in the folder named 'sonel'.
Similarly, the files 'flir_annotation.csv' and 'flir_old_annotation.csv' are based on the images present in the fodlers 'flir' and 'flir_old'
The images can be found as a part of our older work which is presented as an open database [Suranjan Goswami, Nand Kumar Yadav, Satish Kumar Singh. "Thermal Visual Paired Dataset." doi: 10.21227/jjba-6220]
The data is classified into 5 different classes
modern infrastructure: inf:5
In each file, which is presented as an excel file, the data columns are as follows:
filename, file size, file attribute, region count, region id, region shape attributes and region attributes.
region count shows the number of regions present in each image, region attribute presents the details of the rectangle which contains the said attribute and the region attributes presents the attribute name.
These can be directly input into VIA after loading the corresponding database images to see the outlined annotations.
Since the annotation presented by VIA might not be easily usable by all data readers, we have modified the same to be easily processed as the numbers files
These are 'sonel_annotation-numbers.csv', 'flir_annotation-numbers.csv' and 'flir_old_annotation-numbers.csv' .
Here, the class abbreviations are replaced by their corresponding number key as provided above.
Please note that the database we have used contains both registered and unregistered images as a part of the database.
All registered thermal images that have been annotated only, not the unregistered ones as our work required registered thermal images.
This is a one way registration: that is, the annotation done on the thermal images should reflect on the optical images.
We have not included the optical annotation method here, wherein we use DETR to annotate the registered optical images and use the corresponding mapping to create the 2 way annotation.
This database is presented as a part of our work "Novel Deep Learning Method for Thermal to Annotated Thermal-Optical Fused Images"
This is a MATLAB-based tool to convert electrocardiography (ECG) waveforms from paper-based ECG records into digitized ECG signals that is vendor-agnostic. The tool is packaged as an open-source standalone graphical user interface (GUI) based application. This open-source digitization tool can be used to digitize paper ECG records thereby enabling new prediction
This dataset provide researchers a benchmark to develop applicable and adaptive harbor detection algorithms.
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a new 512*256 face sketch dataset