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These differences are summed to create a simple metric of block similarity, the L 1 norm of the difference image or Manhattan distance between two image blocks. The sum of absolute differences may be used for a variety of purposes, such as object recognition, the generation of disparity maps for stereo images, and motion estimation for video ...
A summation-by-parts (SBP) finite difference operator conventionally consists of a centered difference interior scheme and specific boundary stencils that mimics behaviors of the corresponding integration-by-parts formulation. [3] [4] The boundary conditions are usually imposed by the Simultaneous-Approximation-Term (SAT) technique. [5]
The sum of absolute transformed differences (SATD) is a block matching criterion widely used in fractional motion estimation for video compression. It works by taking a frequency transform , usually a Hadamard transform , of the differences between the pixels in the original block and the corresponding pixels in the block being used for comparison.
The absolute difference is used to define other quantities including the relative difference, the L 1 norm used in taxicab geometry, and graceful labelings in graph theory. When it is desirable to avoid the absolute value function – for example because it is expensive to compute, or because its derivative is not continuous – it can ...
Taxicab geometry or Manhattan geometry is geometry where the familiar Euclidean distance is ignored, and the distance between two points is instead defined to be the sum of the absolute differences of their respective Cartesian coordinates, a distance function (or metric) called the taxicab distance, Manhattan distance, or city block distance.
Absolute convergence is important for the study of infinite series, because its definition guarantees that a series will have some "nice" behaviors of finite sums that not all convergent series possess. For instance, rearrangements do not change the value of the sum, which is not necessarily true for conditionally convergent series.
The MAE is conceptually simpler and also easier to interpret than RMSE: it is simply the average absolute vertical or horizontal distance between each point in a scatter plot and the Y=X line. In other words, MAE is the average absolute difference between X and Y.
The mean absolute difference (univariate) is a measure of statistical dispersion equal to the average absolute difference of two independent values drawn from a probability distribution. A related statistic is the relative mean absolute difference , which is the mean absolute difference divided by the arithmetic mean , and equal to twice the ...