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Figure 4 The centers of the correspondence blocks are approximated from the correspondence feature points.
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The points denote correspondence-feature points.
Then a multilevel matching strategy is used to establish the correspondence of feature points.
The homography matrices are provided as the ground truth, so that we can calculate the correspondence between feature points.
A multilevel matching strategy is used to establish the correspondences of feature points.
In order to search for correspondences between feature points belonging to different range images we rely on feature descriptors.
Furthermore, it offers considerable advantages over the checkerboard marker in terms of processing speed, since it makes the correspondence search of feature points and marker-model coordinates, which are required for the pose estimation, redundant.
When comparing the similarity between two images, the first step is to find the correspondence of detected feature points (or features) across the two images.
In our previous work, HD4AR matched a given image to an entire set of base images for finding correspondences between image feature points and 3D points.
In [17], the sparse correspondences are found by feature points and then the dense correspondences are obtained from these sparse matches using the propagation and seed growing methods.
However, better performance might be obtained if the number of correspondences between two image feature points is also considered.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com