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This graph shows that the proposed approach allows us to find quality landmarks using the weight coefficient, e.g., G greater than 5.0 statistically guarantees quality landmarks.
Where high quality landmarks are available, it would be straightforward to incorporate these into the curve estimation procedures.
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However, well-designed evaluation criteria are required to select a quality landmark.
This value can be predefined; thus, quality landmark selection can be automatically achieved.
Methods (a)–(d) ("Landmark selection criteria") were used to determine whether they are suitable for selecting a quality landmark.
Here, to generate a local feature region, the parameters were the same as those described in "Quality landmark selection".
The quality of landmarks should be considered when extracting visual landmarks from image streams.
The proposed method selects quality visual landmarks in a step-by-step manner.
To create a quality visual landmark, the feature region selection process is performed according to the procedure explained in "Landmark candidates collection" and "Landmark selection criteria".
When we slightly changed the parameters, the quality of the landmarks degraded in some scenes even though the changes improved the quality of landmarks in other scenes.
Attractiveness and pleasantness: general appearance/aesthetics, presence of landmarks, quality of the landscape, the variety of activities provided, weather conditions (weather conditions were noted to evaluate how activities and people varied according to season's and weather's changes).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com