10.5281/zenodo.1173885
Long The Nguyen
Irkutsk National Technical University, Lermontov 83, 664074, Irkutsk, Russia
Huong Thu Nguyen
Irkutsk National Technical University, Lermontov 83, 664074, Irkutsk, Russia
Brain Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 4. Flowchart And Results Of Icp Algorithm
Zenodo
2016
Random Forest
3D-model,
artificial intelligence
image processing
data classification
2016-08-16
en
Figure
https://zenodo.org/record/1173886
10.5281/zenodo.1173886
Creative Commons Attribution 4.0
Open Access
<p>The key concept of the standard ICP algorithm can be summarized in two steps: - Compute correspondences between the two scans. - Compute a transformation which minimizes the distance between corresponding points. It is forced to add a maximum matching threshold dmax. In most implementations of ICP, the choice of dmax represents a tradeoff between convergence and accuracy. A low-value result in bad convergence, a large value causes incorrect correspondences to pull the final alignment away from the correct value. Figure 4 describes the steps of the algorithm which determines the point features closest to object boundary. The result of the algorithm is described by images cut from the program (Нгуен, 2016)</p>
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