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The algorithm then constructs a feature vector describing shape based on the relative pairwise distances between all feature points ('Materials and methods').
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The outermost feature points among all feature points generate the red rectangle shown in the new search window.
Nevertheless, the SIFT approach computes several feature points, and not all feature points are appropriate for information embedding.
We first calculate the average of the differences between the dominant orientations for all feature point matches.
(2) The shape vector was constructed as the normalized pairwise distances between all 36 facial feature points.
For similarity measure between two feature points, we adopt the rotation invariant normalized cross-correlation.
This horizontal displacement or disparity between rectified feature points is directly related to the depth of the 3D feature point.
There are angle deviations between the feature points in the vibration velocity signals and those in the cylinder pressure signals.
Multiscale oriented corner correlation (MOCC) is used to evaluate the degree of similarity between the feature points.
The similarity measure decreases monotonically from 1 to 1 with the increase of the difference between two feature points.
It can efficiently estimate the point correspondence and the relevant affine transformational parameters between the feature points of the object shape and the reference shape.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com