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An N-point DFT is expressed as the multiplication =, where is the original input signal, is the N-by-N square DFT matrix, and is the DFT of the signal. The transformation matrix W {\displaystyle W} can be defined as W = ( ω j k N ) j , k = 0 , … , N − 1 {\displaystyle W=\left({\frac {\omega ^{jk}}{\sqrt {N}}}\right)_{j,k=0,\ldots ,N-1 ...
The eight-point algorithm is an algorithm used in computer vision to estimate the essential matrix or the fundamental matrix related to a stereo camera pair from a set of corresponding image points. It was introduced by Christopher Longuet-Higgins in 1981 for the case of the essential matrix.
The name "butterfly" comes from the shape of the data-flow diagram in the radix-2 case, as described below. [1] The earliest occurrence in print of the term is thought to be in a 1969 MIT technical report. [2] [3] The same structure can also be found in the Viterbi algorithm, used for finding the most likely sequence of hidden states.
One can ask whether the DFT matrix is unitary over a finite field. If the matrix entries are over F q {\displaystyle F_{q}} , then one must ensure q {\displaystyle q} is a perfect square or extend to F q 2 {\displaystyle F_{q^{2}}} in order to define the order two automorphism x ↦ x q {\displaystyle x\mapsto x^{q}} .
A useful property of the DFT is that the inverse DFT can be easily expressed in terms of the (forward) DFT, via several well-known "tricks". (For example, in computations, it is often convenient to only implement a fast Fourier transform corresponding to one transform direction and then to get the other transform direction from the first.)
An FFT rapidly computes such transformations by factorizing the DFT matrix into a product of sparse (mostly zero) factors. [2] As a result, it manages to reduce the complexity of computing the DFT from O ( n 2 ) {\textstyle O(n^{2})} , which arises if one simply applies the definition of DFT, to O ( n log n ) {\textstyle O(n\log n)} , where ...
In this case, the DFT simplifies to a more familiar form: S k = ∑ n = 0 N − 1 s [ n ] ⋅ e − i 2 π k N n . {\displaystyle S_{k}=\sum _{n=0}^{N-1}s[n]\cdot e^{-i2\pi {\frac {k}{N}}n}.} In order to take advantage of a fast Fourier transform algorithm for computing the DFT, the summation is usually performed over all N {\displaystyle N ...
where A is the complex starting point, W is the complex ratio between points, and M is the number of points to calculate. Like the DFT, the chirp Z-transform can be computed in O(n log n) operations where = (,). An O(N log N) algorithm for the inverse chirp Z-transform (ICZT) was described in 2003, [4] [5] and in 2019. [6]