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  2. NTU RGB-D dataset - Wikipedia

    en.wikipedia.org/wiki/NTU_RGB-D_dataset

    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 ...

  3. List of datasets in computer vision and image processing

    en.wikipedia.org/wiki/List_of_datasets_in...

    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

  4. Category:Datasets in computer vision - Wikipedia

    en.wikipedia.org/wiki/Category:Datasets_in...

    NTU RGB-D dataset; O. Overhead Imagery Research Data Set; T. Textures: A Photographic Album for Artists and Designers; V. VoTT This page was last edited on 5 May ...

  5. Talk:NTU RGB-D dataset - Wikipedia

    en.wikipedia.org/wiki/Talk:NTU_RGB-D_dataset

    Main page; Contents; Current events; Random article; About Wikipedia; Contact us

  6. Chroma subsampling - Wikipedia

    en.wikipedia.org/wiki/Chroma_subsampling

    In the 480i "NTSC" system, if the luma is sampled at 13.5 MHz, then this means that the Cr and Cb signals will each be sampled at 3.375 MHz, which corresponds to a maximum Nyquist bandwidth of 1.6875 MHz, whereas traditional "high-end broadcast analog NTSC encoder" would have a Nyquist bandwidth of 1.5 MHz and 0.5 MHz for the I/Q channels.

  7. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]

  8. MNIST database - Wikipedia

    en.wikipedia.org/wiki/MNIST_database

    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.

  9. Comparison of color models in computer graphics - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_color_models...

    Note an attribute of the total intensity in the additive model. If full intensity for one projector is 1, then a primary color has a combined intensity of 1. A secondary color has a total intensity of 2. White has a total intensity of 3. Tinting, or "adding white", increases the total intensity of the hue.