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Cristinacce et al.[83], 2008 Local templates per each landmark type are combined into a geometrical configuration.
This feature is then used to train a Gentleboost classifier for each landmark type within its own region of interest (RoI).
However, this "landmark" type of analysis is clinically irrelevant.
We have now conducted additional analyses which showed a significant time (quarters) by landmark type (permanent, transient) interaction (F3,29 = 8.045, p = 0.0005).
We then examined recognition accuracy in each of the four learning quarters and observed a significant time (quarters) by landmark type (permanent, transient) interaction (F3,29 = 8.045, p = 0.0005).
For example, if you happen to know a lot about a certain building, landmark, type of animal, or refreshment, share your knowledge.
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In Figure 7, a more detailed comparison based on landmark types is given.
We show that our method clearly outperforms standard AAM fitting and provides reasonable tracking results for all landmark types.
The Web-based search engine is able to search for locations based on area names, building names, and groups of landmark types, business names, and business categories.
The overall performance is averaged over all landmark types P = 100 ∑ k = 1 K ∑ i = 1 I [ i : δ i k < Th ] K × I. (3).
From the quantitative results in Figure 8a, it can be seen that the median point-to-point error of all landmark types is below 5 mm.
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