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The CIE 1976 color difference formula is the first formula that related a measured color difference to a known set of CIELAB coordinates. This formula has been succeeded by the 1994 and 2000 formulas because the CIELAB space turned out to be not as perceptually uniform as intended, especially in the saturated regions.
Specifically, it is the standard deviation of a number of experimental color matches to the central color. Assuming a bivariate normal distribution of these match points, a MacAdam ellipse thus contains about 39% of the color match points. A 2X MacAdam ellipse will contain about 86% of the match points, and a 3X MacAdam ellipse will contain ...
The mean and the standard deviation of a set of data are descriptive statistics usually reported together. In a certain sense, the standard deviation is a "natural" measure of statistical dispersion if the center of the data is measured about the mean. This is because the standard deviation from the mean is smaller than from any other point.
Computing the features - Use the color moments formulae in order to compute the first three moments for each of the color channels in the image. For example, if the HSV color space is used, this means that for each of the images, 9 features in total will be computed (the first three order moments for the Hue, Saturation, and Value channels).
The unbiased estimation of standard deviation is a technically involved problem, though for the normal distribution using the term n − 1.5 yields an almost unbiased estimator. The unbiased sample variance is a U-statistic for the function f ( y 1 , y 2 ) = ( y 1 − y 2 ) 2 /2 , meaning that it is obtained by averaging a 2-sample statistic ...
The use of n − 1 instead of n in the formula for the sample variance is known as Bessel's correction, which corrects the bias in the estimation of the population variance, and some, but not all of the bias in the estimation of the population standard deviation.
Multiple regression equations are used to describe the relationship between sample locations and environmental variables, often relying on geographic information systems (GIS) to collect measurements. This results in an equation that can predict pollution concentrations at unmeasured locations based on data for the predictor variables in ...
Common examples of measures of statistical dispersion are the variance, standard deviation, and interquartile range. For instance, when the variance of data in a set is large, the data is widely scattered. On the other hand, when the variance is small, the data in the set is clustered.