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This landmark distance model provides a new basis for representation of fiber tracts and can be used for detection and prediction of fiber tracts based on landmarks.
Table 4 shows the average pairwise image registration error (i.e., landmark distance) for the whole experiment.
Table 4 Statistics of landmarks error (in mm) using deformable group-wise registration of segmented CT images Landmark distance Min (mm) Mean (mm) Max (mm) Std (mm) Rabbit 1.01 2.66 4.50 1.74 Ferret 1.86 3.93 4.65 1.24 Mouse 0.23 2.52 6.0.23.62.
The nearest landmark distance was slightly greater for walking, and when walking and cycling were combined, and for auto-rickshaw.
For all types of motorized travel, the 'nearest landmark' distance was shorter than the 'in-depth interview' distance, with the exception of auto rickshaw, perhaps due to its ability to take short-cut routes, possibly leading to traffic violations [ 35].
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The method is based on a paraconsistent artificial neural network model that considers as input preprocessed information from measurement data on landmark distances, possibly generated by different sensors in different robots and considering different metrics.
Landmark distances and angles are learned, modeled as Gaussians and the information embedded in a graph.
Initialized landmark positions are corrected via shape descriptors of Gaussian curvature, mean curvature, surface normals and landmark distances to the face symmetry plane.
The minimum (min), mean, maximum (max), and standard deviation (std) of the landmark distances between baseline and follow-up scans after deformable registration were recorded.
The landmark-based registration errors show that the proposed registration framework - using parallel transportation of deformation fields from CT-to-CT registration into PET-to-PET registration - had high registration accuracy as shown by the small landmark distances.
Distance traveled measured in pixels between successive frames was computed, and converted into centimeters using known landmark distances in the video frame.
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