Image Processing
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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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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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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1.Visualization of convolutional neural network layers for one participant at ROI 301 * 301
2.Convolutional neural network structure analysis in Matlab
3.Convolutional neural network Matlab code
4.Videos of brightness mode (B-mode) ultrasound images from two participants during the recorded walking trials at 5 different speeds
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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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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.
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