Image Processing
<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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Supplementary materials for manuscript: Self-absorption Correction in X-ray Fluorescence Computed Tomography with Deep Convolutional Neural Network,
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Medical Symptoms are sometimes very tricky to analyse in real time as it takes time for example, first to detect the symptoms then to perform some tests and finally coming to a solution. This process can be eliminated and lot of time can be saved by introducing the concept of Deep learning. CNNs create a network for extracting the features of a given image in order to evaluate the image based on the conditions required. This property of the CNN is used as a certain advantage in order to detect the symptoms based on the type of X-ray images provided.
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The Objects Mosaic Hyperspectral Database contains 10,666 hyperspectral cubes of size 256x256x29 in the 420-700nm spectral range. This original hyperspectral database of real objects was experimentally acquired as described in the paper "SHS-GAN: Synthetic enhancement of a natural hyperspectral database", by J. Hauser, G. Shtendel, A. Zeligman, A. Averbuch, and M. Nathan, in the IEEE Transactions on Computational Imaging.
In addition, the database contains the SHS-GAN algorithm, which enables to generate synthetic database of hyperspectral images.
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The dataset consists of 751 videos, each containing the performance one of the handball actions out of 7 categories (passing, shooting, jump-shot, dribbling, running, crossing, defence). The videos were manually extracted from longer videos recorded in handball practice sessions.
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Of late, efforts are underway to build computer-assisted diagnostic tools for cancer diagnosis via image processing. Such computer-assisted tools require capturing of images, stain color normalization of images, segmentation of cells of interest, and classification to count malignant versus healthy cells. This dataset is positioned towards robust segmentation of cells which is the first stage to build such a tool for plasma cell cancer, namely, Multiple Myeloma (MM), which is a type of blood cancer. The images are provided after stain color normalization.
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This dataset contains images obtained in image processing experiments presented in our paper "A Hybrid Radix–4 and Approximate Logarithmic Multiplier for Energy Efficient Image Processing", which is currently under review.
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WITH the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.
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