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  2. Color histogram - Wikipedia

    en.wikipedia.org/wiki/Color_histogram

    Color histograms are flexible constructs that can be built from images in various color spaces, whether RGB, rg chromaticity or any other color space of any dimension. A histogram of an image is produced first by discretization of the colors in the image into a number of bins, and counting the number of image pixels in each bin.

  3. Plotting algorithms for the Mandelbrot set - Wikipedia

    en.wikipedia.org/wiki/Plotting_algorithms_for...

    The top row is a series of plots using the escape time algorithm for 10000, 1000 and 100 maximum iterations per pixel respectively. The bottom row uses the same maximum iteration values but utilizes the histogram coloring method. Notice how little the coloring changes per different maximum iteration counts for the histogram coloring method plots.

  4. Image color transfer - Wikipedia

    en.wikipedia.org/wiki/Image_color_transfer

    Color transfer processing can serve two different purposes: one is calibrating the colors of two cameras for further processing using two or more sample images, the second is adjusting the colors of two images for perceptual visual compatibility. Color calibration is an important pre-processing task in computer vision applications. Many ...

  5. Image histogram - Wikipedia

    en.wikipedia.org/wiki/Image_histogram

    An image histogram is a type of histogram that acts as a graphical representation of the tonal distribution in a digital image. [1] It plots the number of pixels for each tonal value. By looking at the histogram for a specific image a viewer will be able to judge the entire tonal distribution at a glance.

  6. Normalization (image processing) - Wikipedia

    en.wikipedia.org/wiki/Normalization_(image...

    max is the maximum value for color level in the input image within the selected kernel. min is the minimum value for color level in the input image within the selected kernel. [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.

  7. 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.

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  9. Histogram of oriented gradients - Wikipedia

    en.wikipedia.org/wiki/Histogram_of_oriented...

    The HOG descriptor is then the concatenated vector of the components of the normalized cell histograms from all of the block regions. These blocks typically overlap, meaning that each cell contributes more than once to the final descriptor. Two main block geometries exist: rectangular R-HOG blocks and circular C-HOG blocks.