Image Processing

This CoFSM dataset contains the supplemental material of TIP3157450 (Multimodal remote sensing image datasets). The CoFSM dataset contains six types of modal images (multi temporal-optical, infrared-optical, depth-optical, map-optical, SAR-optical andnight-day). Each modal type includes 10 groups of images, and each set of images has corresponding ground truth points.

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This dataset consists of 1878 labeled images of flowers from blackberry trees from the specie Rubus L. subgenus Rubus Watson. These are white flowers with five petals that blossom in the spring through summer. The images were collected using an Intel RealSense D435i camera inside a greenhouse.

This images were inicially collected to support a robotic autonomous pollination project.

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The experiment is based on the open source RSRP data provided by Huawei Technologies Co., LTD. It measures RSRP of 415,244 signal receiving points in 180 dense urban communication cells. 

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 We provided a new set of extreme low-light datasets of short exposure raw images in RGB format and long-exposure reference images of the same scenes.

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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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