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The NTU RGB-D (Nanyang Technological University's Red Blue Green and Depth information) dataset is a large dataset containing recordings of labeled human activities. [1] This dataset consists of 56,880 action samples containing 4 different modalities (RGB videos, depth map sequences, 3D skeletal data, infrared videos) of data for each sample.
Linnaeus 5 dataset Images of 5 classes of objects. Classes labelled, training set splits created. 8000 Images Classification 2017 [40] Chaladze & Kalatozishvili 11K Hands 11,076 hand images (1600 x 1200 pixels) of 190 subjects, of varying ages between 18 – 75 years old, for gender recognition and biometric identification. None 11,076 hand images
NTU RGB-D dataset; O. Overhead Imagery Research Data Set; T. Textures: A Photographic Album for Artists and Designers ... This page was last edited on 5 May 2023, at ...
3.4 TB English text, 1.4 TB Chinese text, 1.1 TB Russian text, 595 MB German text, 431 MB French text, and data for 150+ languages (figures for version 23.01) JSON Lines [458] Natural Language Processing, Text Prediction 2021 [459] [460] Ortiz Suarez, Abadji, Sagot et al. OpenWebText An open-source recreation of the WebText corpus.
The set of images in the MNIST database was created in 1994. Previously, NIST released two datasets: Special Database 1 (NIST Test Data I, or SD-1); and Special Database 3 (or SD-2). They were released on two CD-ROMs. SD-1 was the test set, and it contained digits written by high school students, 58,646 images written by 500 different writers.
Range imaging is the name for a collection of techniques that are used to produce a 2D image showing the distance to points in a scene from a specific point, normally associated with some type of sensor device.
RGB channels roughly follow the color receptors in the human eye, and are used in computer displays and image scanners. If the RGB image is 24-bit (the industry standard as of 2005), each channel has 8 bits, for red, green, and blue—in other words, the image is composed of three images (one for each channel), where each image can store ...
[4] Local contrast stretching considers each range of color palate in the image (R, G, and B) separately, providing a set of minimum and maximum values for each color palate. Global Contrast Stretching, on the other hand, considers all color palate ranges at once to determine the maximum and minimum values for the entire RGB color image.