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A histogram is a visual representation of the distribution of quantitative data. The term was first introduced by Karl Pearson. [ 1] To construct a histogram, the first step is to "bin" (or "bucket") the range of values— divide the entire range of values into a series of intervals—and then count how many values fall into each interval.
For digital images, a color histogram represents the number of pixels that have colors in each of a fixed list of color ranges, that span the image's color space, the set of all possible colors. The color histogram can be built for any kind of color space, although the term is more often used for three-dimensional spaces such as RGB or HSV.
Histogram equalization accomplishes this by effectively spreading out the highly populated intensity values which are used to degrade image contrast. The method is useful in images with backgrounds and foregrounds that are both bright or both dark. In particular, the method can lead to better views of bone structure in x-ray images, and to ...
In statistics, the Freedman–Diaconis rulecan be used to select the width of the bins to be used in a histogram.[1] It is named after David A. Freedmanand Persi Diaconis. For a set of empirical measurements sampled from some probability distribution, the Freedman–Diaconis rule is designed approximately minimize the integral of the squared ...
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.
The histogram is itself a maximum-likelihood (ML) estimate of the discretized frequency distribution [citation needed]), where is the width of the th bin. Histograms can be quick to calculate, and simple, so this approach has some attraction.
Histogram matching. In image processing, histogram matching or histogram specification is the transformation of an image so that its histogram matches a specified histogram. [1] The well-known histogram equalization method is a special case in which the specified histogram is uniformly distributed. [2]
Adaptive histogram equalization (AHE) is a computer image processing technique used to improve contrast in images. It differs from ordinary histogram equalization in the respect that the adaptive method computes several histograms, each corresponding to a distinct section of the image, and uses them to redistribute the lightness values of the image.
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