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Because their complexity depends on the number of landmark points, one can reduce the complexity by computing simplicial complexes using a smaller number of vertices.
Number of landmarks and their accuracy requirements: The accuracy requirements and the number of landmark points vary based on the intended application.
Since the displacement of landmark point in each frame depends on each individual, we use the Inter-ocular Distance (IOD) for size normalization.
The correspondence between landmark points on the facial surface was determined automatically via the arclength map.
This approach is much simpler than deformation models, such as non-rigid 3D image registration based on the identification and matching of landmark-points and the generation of a deformation field such as a thin-plate-spline based on the matching.
So far this image integration is based on registration involving landmark points and surface alignment.
Evaluation of the registration processes was based on precisely defined anatomical landmark points by expert clinicians.
We select 26 landmark points on the facial region to facilitate the analysis of human facial expressions.
There are 29 manually landmarked points, and this number can go up to 35 whenever landmark points on the ear are visible.
We implement an image processing front-end to locate and track eight anatomical landmark points on depth image streams acquired by the ToF camera.
Six landmark points on the skull (three for each plane) were measured with a Micro-Scribe-3DX coordinate data stylus (Immersion Corp., Can Jose, CA, USA).
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