Image Processing
Rembrandt contains data generated through the Glioma Molecular Diagnostic Initiative from 874 glioma specimens comprising approximately 566 gene expression arrays, 834 copy number arrays, and 13,472 clinical phenotype data points. These data are currently housed in Georgetown University's G-DOC System and are described in a related manuscript .
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The dermoscopic images considered in the paper "Dermoscopic Image Classification with Neural Style Transfer" are available for public download through the ISIC database (https://www.isic-archive.com/#!/topWithHeader/wideContentTop/main). These are 24-bit JPEG images with a typical resolution of 768 × 512 pixels. However, not all the images in the database are in satisfactory condition.
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Recent works that utilized deep models have achieved superior results in various image restoration (IR) applications. Such approach is typically supervised, which requires a corpus of training images with distributions similar to the images to be recovered. On the other hand, the shallow methods, which are usually unsupervised remain promising performance in many inverse problems, \eg, image deblurring and image compressive sensing (CS), as they can effectively leverage nonlocal self-similarity priors of natural images.
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Reverse transcription-polymerase chain reaction (RT-PCR) is currently the gold standard in COVID-19 diagnosis. It can, however, take days to provide the diagnosis, and false negative rate is relatively high. Imaging, in particular chest computed tomography (CT), can assist with diagnosis and assessment of this disease. Nevertheless, it is shown that standard dose CT scan gives significant radiation burden to patients, especially those in need of multiple scans.
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The dataset consists of two classes: COVID-19 cases and Healthy cases
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2026 Views
DATA PROVIDED PRIOR TO ACCEPTANCE OF THE ASSOCIATED MANUSCRIPT.
This dataset contains video sequences and stereo reconstruction results supporting the IEEE Access contribution "Stereo laryngoscopic impact site prediction for droplet-based stimulation of the laryngeal adductor reflex" (J. F. Fast et al.).
See readme file for further information.
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This contains data corresponding to the paper Multi-Resolution Data Fusion for Super-Resolution Imaging.
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Without publicly available dataset, specifically in handwritten document recognition (HDR), we cannot make a fair and/or reliable comparison between the methods. Considering HDR, Indic script’s document recognition is still in its early stage compared to others such as Roman and Arabic. In this paper, we present a page-level handwritten document image dataset (PHDIndic_11), of 11 official Indic scripts: Bangla, Devanagari, Roman, Urdu, Oriya, Gurumukhi, Gujarati, Tamil, Telugu, Malayalam and Kannada.
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An offline handwritten signature dataset from two most popular scripts in India namely Roman and Devanagari is proposed here.
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<p>The dataset comprises 2035 images from 14 different software architectural patterns (100+ images each), viz., Broker, Client Server, Microkernel, Repository, Publisher-Subscriber, Peer-to-Peer, Event Bus, Model View Controller, REST, Layered, Presentation Abstraction Controller, Microservices, and Space-based patterns.</p>
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799 Views