phaseCorrelate

Image processingfunctionOpenCV 5.0.0

import { phaseCorrelate } from '@banou/opencv-wasm'

Use after await initOpenCV(). See the initialization and named imports guide.

Explore visually: Phase correlation →

Estimate translation from the phase relationship between two images in the frequency domain.

VISUAL WALKTHROUGHMatching

REFERENCE INPUTTwo shifted signals

Orange is shifted seven samples relative to violet.

→

RESULTLocate the correlation peak

The ideal inverse transform concentrates the response at the displacement. Real images produce a broader, noisy peak.

Illustrative example. The stages explain the method; they are not a live OpenCV execution.

example: samples/cpp/snippets/phase_corr.cpp An example using the phaseCorrelate function The function is used to detect translational shifts that occur between two images.

The operation takes advantage of the Fourier shift theorem for detecting the translational shift in the frequency domain. It can be used for fast image registration as well as motion estimation. For more information please see https://en.wikipedia.org/wiki/Phase_correlation

Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed with getOptimalDFTSize.

The function performs the following equations:

• First it applies a Hanning window to each image to remove possible edge effects, if it's provided by user. See createHanningWindow and https://en.wikipedia.org/wiki/Hann_function. This window may be cached until the array size changes to speed up processing time.
• Next it computes the forward DFTs of each source array:

\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}

where \mathcal{F} is the forward DFT.

• It then computes the cross-power spectrum of each frequency domain array:

R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}

• Next the cross-correlation is converted back into the time domain via the inverse DFT:

r = \mathcal{F}^{-1}\{R\}

• Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to achieve sub-pixel accuracy.

(\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}

• If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5 centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single peak) and will be smaller when there are multiple peaks.

See: dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow

phaseCorrelate(src1: Mat, src2: Mat, window: Mat): phaseCorrelateResult;

2 available overloads

phaseCorrelate(src1: Mat, src2: Mat): phaseCorrelateResult;

phaseCorrelate(src1: Mat, src2: Mat, window: Mat): phaseCorrelateResult;

src1

Source floating point array (CV_32FC1 or CV_64FC1)

src2

Source floating point array (CV_32FC1 or CV_64FC1)

window

Floating point array with windowing coefficients to reduce edge effects (optional).

Returns

detected phase shift (sub-pixel) between the two arrays. Scalar output parameters are returned as named fields in this object. Release returned native handles with using or delete(), including handles nested in results.

Upstream declaration ↗

These signatures describe this package. Upstream documentation can mention optional backends that are absent from this build. Check runtime compatibility before choosing a backend or file format.