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Euclidean distance D to group two feature points ("Distinctive feature region extraction based on feature point grouping") was set to (sqrt{10}).
The parameters used to select the local feature region were experimentally defined as follows: Euclidean distance D to group two feature points ("Distinctive feature region extraction based on feature point grouping") was set to (sqrt{10}).
The PGI algorithm is employed to identify point groups and their feature points in an initial point set by combining subspace clustering analysis and the graph theory.
For each group calculate the homography matrix by using the feature points within the group.
Instead of using the inspection spokes to do tooth segmentation, which may lead to unsatisfactory cut results, we utilize them to separate those feature points into different groups (each group corresponding to one tooth).
The proposed clustering algorithm can successfully separate feature points into groups with reasonable sizes and ranges.
Suppose that there are m feature points in the first group and n feature points in the second group.
A robust, computer-aided, automatic identification method [ 27] is used in our framework to identify them, as demonstrated by white spheres and a blue plane in Figure 2. The inspection spoke-based strategy [ 20] is then employed to automatically separate these teeth feature points into different groups.
Another feature points people to local groups, and a related third one points users in these community-oriented groups to local news.
Each line segment connecting two feature point groups denotes one group-to-group matching operation.
Feature points are divided into 4 groups according to the pyramid level at which they are detected.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com