Image Processing Reference

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FIGURE 2
Distance map [mm] (left column) and correspondence map [number of units] (right

column) for different modifications of ICP computing correspondence: Euclidean distance (E),

normal shooting with initial rigid registration (NH), static marker vectors (SM), and dynamic

marker vectors (DM).

Because it is difficult to measure directly the quality of correspondences, observation was

feature, number of correspondences assigned to every target point (desirable value is 1). It

is easier to compare correspondence map globally with different cases using correspondence

map histogram (
Figure 3
). Average correspondence assignment error of points nearest to the

markers allows to measure the quality of correspondence points from cloud, which are nearest

to the markers.

FIGURE 3
Distance map histogram [mm] (a) and correspondence map histogram [number of

units], (b) in different modifications of ICP computing correspondence: Euclidean distance (E),

normal shooting with initial rigid registration (NH), static marker vectors (SM), and dynamic

marker vectors (DM).

The results show improvement when markers are used not only in computing transform-

make the proposed changes more universal k-nearest neighbor method and radius constraint

could be used to apply the marker information to not only every point in the cloud but also

the nearest points to the markers. For points that are not near a marker, the Euclidean dis-

tance was used. The score results for three selected values (i.e., 5, 10, and 15%) of the radius

constraint selected as percent of the cloud width were calculated. The results are presented in

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