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Histogram. 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 ...
less than 10 6 is 837 799, which has 524 steps, less than 10 7 is 8 400 511, which has 685 steps, less than 10 8 is 63 728 127, which has 949 steps, less than 10 9 is 670 617 279, which has 986 steps, less than 10 10 is 9 780 657 630, which has 1132 steps, [10] less than 10 11 is 75 128 138 247, which has 1228 steps, less than 10 12 is 989 345 ...
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 ...
Entropy estimation. In various science/engineering applications, such as independent component analysis, [1] image analysis, [2] genetic analysis, [3] speech recognition, [4] manifold learning, [5] and time delay estimation [6] it is useful to estimate the differential entropy of a system or process, given some observations.
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.
The normal probability plot is a graphical technique to identify substantive departures from normality. This includes identifying outliers, skewness, kurtosis, a need for transformations, and mixtures. Normal probability plots are made of raw data, residuals from model fits, and estimated parameters. In a normal probability plot (also called a ...
Bootstrapping (statistics) Bootstrapping is a procedure for estimating the distribution of an estimator by resampling (often with replacement) one's data or a model estimated from the data. [1] Bootstrapping assigns measures of accuracy (bias, variance, confidence intervals, prediction error, etc.) to sample estimates. [2][3] This technique ...
v. t. e. The histogram of oriented gradients (HOG) is a feature descriptor used in computer vision and image processing for the purpose of object detection. The technique counts occurrences of gradient orientation in localized portions of an image. This method is similar to that of edge orientation histograms, scale-invariant feature transform ...