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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...

    [3] Torralba et al. JFT-300M Dataset internal to Google Research. 300M images with 375M labels in 18291 categories 300,000,000 image, label 2017 [4] Google Research Places: 10+ million images in 400+ scene classes, with 5000 to 30,000 images per class. 10,000,000 image, label 2018 [5] Zhou et al Ego 4D

  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. 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.

  6. 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

  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. 8-bit color - Wikipedia

    en.wikipedia.org/wiki/8-bit_color

    The simplest form of quantization is to simply assign 3 bits to red, 3 bits to green and 2 bits to blue, as the human eye is less sensitive to blue light. This creates a so called 3-3-2 8-bit color image, arranged like on the following table: Bit 7 6 5 4 3 2 1 0 Data R R R G G G B B. This process is sub optimal.

  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.