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An example of 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 .
Fig. 1. HSL (a–d) and HSV (e–h). Above (a, e): cut-away 3D models of each. Below: two-dimensional plots showing two of a model's three parameters at once, holding the other constant: cylindrical shells (b, f) of constant saturation, in this case the outside surface of each cylinder; horizontal cross-sections (c, g) of constant HSL lightness or HSV value, in this case the slices halfway ...
Figure 2. Box-plot with whiskers from minimum to maximum Figure 3. Same box-plot with whiskers drawn within the 1.5 IQR value. A boxplot is a standardized way of displaying the dataset based on the five-number summary: the minimum, the maximum, the sample median, and the first and third quartiles.
In the classical boxplot, the box itself represents the middle 50% of the data. Since the data ordering in the contour boxplot is from the center outwards, the 50% central region is defined by the band delimited by the 50% of deepest, or the most central observations.
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 case of exact graph matching is known as the graph isomorphism problem. [1] The problem of exact matching of a graph to a part of another graph is called subgraph isomorphism problem . Inexact graph matching refers to matching problems when exact matching is impossible, e.g., when the number of vertices in the two graphs are different.
In some literature, the term complete matching is used. In the above figure, only part (b) shows a perfect matching. A perfect matching is also a minimum-size edge cover. Thus, the size of a maximum matching is no larger than the size of a minimum edge cover: () . A graph can only contain a perfect matching when the graph has an even ...
In statistical graphics, the functional boxplot is an informative exploratory tool that has been proposed for visualizing functional data. [1] [2] Analogous to the classical boxplot, the descriptive statistics of a functional boxplot are: the envelope of the 50% central region, the median curve and the maximum non-outlying envelope.
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