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  2. Scott's rule - Wikipedia

    en.wikipedia.org/wiki/Scott's_Rule

    Scott's rule is a method to select the number of bins in a histogram. [1] Scott's rule is widely employed in data analysis software including R , [ 2 ] Python [ 3 ] and Microsoft Excel where it is the default bin selection method.

  3. Sturges's rule - Wikipedia

    en.wikipedia.org/wiki/Sturges's_rule

    Sturges's rule [1] is a method to choose the number of bins for a histogram.Given observations, Sturges's rule suggests using ^ = + ⁡ bins in the histogram. This rule is widely employed in data analysis software including Python [2] and R, where it is the default bin selection method.

  4. Local binary patterns - Wikipedia

    en.wikipedia.org/wiki/Local_binary_patterns

    Multi-block LBP: the image is divided into many blocks, a LBP histogram is calculated for every block and concatenated as the final histogram. Volume Local Binary Pattern(VLBP): [ 11 ] VLBP looks at dynamic texture as a set of volumes in the (X,Y,T) space where X and Y denote the spatial coordinates and T denotes the frame index.

  5. Histogram - Wikipedia

    en.wikipedia.org/wiki/Histogram

    In a histogram, each bin is for a different range of values, so altogether the histogram illustrates the distribution of values. But in a bar chart, each bar is for a different category of observations (e.g., each bar might be for a different population), so altogether the bar chart can be used to compare different categories.

  6. Otsu's method - Wikipedia

    en.wikipedia.org/wiki/Otsu's_method

    This threshold is determined by minimizing intra-class intensity variance, or equivalently, by maximizing inter-class variance. [2] Otsu's method is a one-dimensional discrete analogue of Fisher's discriminant analysis, is related to Jenks optimization method, and is equivalent to a globally optimal k-means [3] performed on the intensity histogram.

  7. Bag-of-words model in computer vision - Wikipedia

    en.wikipedia.org/wiki/Bag-of-words_model_in...

    In computer vision, the bag-of-words model (BoW model) sometimes called bag-of-visual-words model [1] [2] can be applied to image classification or retrieval, by treating image features as words. In document classification , a bag of words is a sparse vector of occurrence counts of words; that is, a sparse histogram over the vocabulary.

  8. Senator says Trump cannot ignore law requiring ByteDance to ...

    www.aol.com/news/senator-says-trump-cannot...

    WASHINGTON (Reuters) -President-elect Donald Trump cannot ignore a law requiring Chinese-based ByteDance to divest its popular short video app TikTok in the U.S. by early next year or face a ban ...

  9. Histogram equalization - Wikipedia

    en.wikipedia.org/wiki/Histogram_equalization

    For example, if applied to 8-bit image displayed with 8-bit gray-scale palette it will further reduce color depth (number of unique shades of gray) of the image. Histogram equalization will work the best when applied to images with much higher color depth than palette size, like continuous data or 16-bit gray-scale images. There are two ways to ...