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.

Instructions: 

We manually annotate all images 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).] for the creation of the box.

 

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

 

Class:Abbreviation:Key 

modern infrastructure: inf:5

crowd: cro:4

human:hum:3

animal:ani:2

nature:nat:1

 

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.

 

We also include 3 ZIP files with the images and their corresponding annotations both manually and done with DETR.

All annotations are labelled as NAME, X_START coordinate, Y_START coordinate, WIDTH, HEIGHT, CLASS for the individual manual annotations.

FOr the DETR annotations, they correspond to NAME, X_START coordinate, Y_START coordinate, X_END coordinate, Y_END coordinate, CLASS.

 

This database is presented as a part of our work "Novel Deep Learning Method for Thermal to Annotated Thermal-Optical Fused Images"

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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

algorithms.

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This dataset  provide researchers a benchmark to develop applicable and adaptive harbor detection algorithms.

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<p>This is the image dataset for satellite image processing&nbsp; which is a collection therml infrared and multispectral images .</p>

Instructions: 

Dataset images
Thermal infrared images and multispectral images
image size:512x512
format:
image:.tiff
file :.h5

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In the field of 3D reconstruction, although there exist some standard datasets for evaluating the segmentation results of close-up 3D models, these datasets cannot be used to evaluate the segmentation results of 3D models based on satellite images. To address this issue, we provide a standard dataset for evaluating the segmentation results of satellite images and their corresponding DSMs. In this dataset, the satellite images maintain an exact correspondence with the DSMs, thus the segmentation results of both satellite images and DSMs can be evaluated by our proposed dataset.

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a new 512*256 face sketch dataset

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DIDA is a new image-based historical handwritten digit dataset and collected from the Swedish historical handwritten document images between the year 1800 and 1940. It is the largest historical handwritten digit dataset which is introduced to the Optical Character Recognition (OCR) community to help the researchers to test their optical handwritten character recognition methods. To generate DIDA, 250,000 single digits and 200,000 multi-digits are cropped from 75,000 different document images. 

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The accompanying dataset for the CVSports 2021 paper: DeepDarts: Modeling Keypoints as Objects for Automatic Scoring in Darts using a Single Camera

Paper Abstract:

Instructions: 

The recommended way to load the labels is to use the pandas Python package:

import pandas as pd

labels = pd.read_pickle("labels.pkl")

See github repository for more information: https://github.com/wmcnally/deep-darts

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We present a 4D Light Field (LF) video dataset collected by the camera matrix, to be used for designing and testing algorithms and systems for LF video coding and processing. For collecting these videos, a 10x10 LF capture matrix composed of 100 cameras is designed and implemented, and the resolution of each camera is 1920x1056. The videos are taken in real and varying illumination conditions. The dataset contains a total of nine groups of LF videos, of which eight groups are collected with a fixed camera matrix position and a fixed orientation.

Instructions: 

The stream data of all LF videos are stored under the directory LF_video_data, and each video contains 100 stream files. The stream files are in 1920 x 1056 24 frames per second MP4 format without audio. There are 100 mat files in the Calibration_mat directory, which store all camera parameters of 100 lenses within the matrix. The depth estimation results are stored under the directory Depth_evaluation_result, which is for reference only.

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