Image Processing

This dataset was prepared to aid in the creation of a machine learning algorithm that would classify the white blood cells in thin blood smears of juvenile Visayan warty pigs. The creation of this dataset was deemed imperative because of the limited availability of blood smear images collected from the critically endangered species on the internet. The dataset contains 3,457 images of various types of white blood cells (JPEG) with accompanying cell type labels (XLSX).

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This repository contains the data related to the paper “CNN-Based Image Reconstruction Method for Ultrafast Ultrasound Imaging” (10.1109/TUFFC.2021.3131383). It contains multiple datasets used for training and testing, as well as the trained models and results (predictions and metrics). In particular, it contains a large-scale simulated training dataset composed of 31000 images for the three different imaging configuration considered (i.e., low quality, high quality, and ultrahigh quality).

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Automated driving in public traffic still faces many technical and legal challenges. However, automating vehicles at low speeds in controlled industrial environments is already achievable today. A reliable obstacle detection is mandatory to prevent accidents. Recent advances in convolutional neural network-based algorithms have made it conceivable to replace distance measuring laser scanners with common monocameras.

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Hyperspectral Image Dataset for Focus Analysis (HIDFA) is composed by different hyperspectral (HS) images, acquired varying the working distance from the objective lens to produce different blurriness levels (11). HS images were captured from commercial samples (Brunel microscopes Ltd, England, UK) which include rat histology, leaf structures, stems, blood smears, and freshwater algae. Captures were taken using 5×, 10× and 20× magnification lenses in a HS microscope previously employed for different histology applications.

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ViFoDAC is a collection of Authentic videos and Forged videos. The dataset has a total of 16 Authentic videos and 16 Forged videos. The Authentic videos are camera recorded whereas the Forged videos are edited using Adobe Premiere Pro and Wondershare Filmora software. The dataset can be used to train and optimise video identification models. This dataset can be used for the Research and Development of fake video classification. 

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ATTENTION: THIS DATASET DOES NOT HOST ANY SOURCE VIDEOS. WE  PROVIDE ONLY HIDDEN FEATURES GENERATED BY PRE-TRAINED DEEP MODELS AS DATA

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Re-curated Breast Imaging Subset DDSM Dataset (RBIS-DDSM) is a curated version of 849 images from the CBIS-DDSM dataset available online with a permissive copyright license (CC-BY-SA 3.0). The  CBIS-DDSM dataset is an improved version of the DDSM dataset. The authors of the CBIS-DDSM dataset attempted to improve the ground truth by applying simple image processing based methods to enhance the edges without any manual intervention from medical experts in order to segment and annotate masses. However, these annotations (segmentation maps) are inaccurate in most of the images. 

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Part of the list-mode dataset for the simulation model

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