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Landmarks consisted of boundaries or points that were considered homologous across Rhabditina as predicted by fine-structural anatomy (Baldwin et al., 1997; Ragsdale and Baldwin, 2010); stomatal terminology follows De Ley et al. (1995).
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These landmarks consist of a reduced set of key points, obtained automatically as in [6]. Figure 6 shows an example of the situation of these 14 points.
Our method relies on high level visual landmarks consisting of appearance invariant descriptors that are extracted by a pre-trained Convolutional Neural Network (CNN) on the basis of image patches.
One approach is to use a suitable set of landmarks consisting of noticeable facial parts such as the eyes, nose, and mouth as well as the relative locations and the statistics of local neighboring pixels of these landmarks to construct feature vectors containing relevant and nonredundant information [8, 9].
Twenty-four homolandmarksndmarks, consisting of 12 fixed and 12 semi-landmarks were defined that describe the external LPJ shape along with the dentigerous area (Additional file 1: Figure S1).
These landmarks, consisting of the central sulcus, sylvian fissure, anterior portion of the superior temporal gyrus, calcarine sulcus, and dorsal and ventral portions of the medial wall, were drawn on smoothed or flattened surfaces using information visible in other surface configurations (midthickness, inflated) as well as the segmentation and MRI volumes.
Using a visual landmark consisting of dozens of neighboring feature points, two evaluation criteria were considered: distinctiveness and repeatability.
For this study, the definition of the Rv coefficient was the correlation between two landmarks each consisting of x and y variables in two dimensions (for more general explanations, see references [ 86- 90]).
At this stage, landmark knowledge consists of familiarity with the appearance and perhaps the names of the landmarks, but no knowledge of how those landmarks are spatially related (assuming they are out of sight from each other).
Landmark initialization consists of defining the initial coordinates and the initial covariance of landmarks (interest points).
The first step consisted of landmark digitization on each model through VAM application version 2.8.3 Canfield Scientific Inc.., Fairfield-NJ, USA).
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