Image Processing
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.
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This dataset was acquired at the Radboud University Medical Center, Nijmegen, the Netherlands and enriched with landmarks by Fraunhofer MEVIS. It consists of nine datasets of consecutive sections, each containing four slides stained with H&E, CD8, CD45, Ki67, respectively.
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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).
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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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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 which is a collection therml infrared and multispectral images .</p>
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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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