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We apply the proposed definition evaluation method to compute the definition values of these captured images.
We fit the image definition values vs. search positions to a quadratic polynomial equation as follows: y=a+bx+c{x}^2 (5).
Figure 3a shows a curve of sampled image definition values in the auto-focusing of the brake pad image shown in Fig. 1.
The set of image definition values are denoted as ( left{Fleft {P}_0^1right), Fleft {P}_1^1right),dots, Fleft {P}_n^1right)right} ).
Figure 2 shows the curves of these image definition values from low to high and from high to low, computed with different methods.
Thus, the local peaks of the image definition values can be effectively reduced to facilitate the search of the best focus.
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Trade secrets are by definition valued by their owners.
The image definition value is calculated at the searching step, and the optimal position with the maximum definition value is obtained as ( {P}_m^{t+1} ).
The proposed definition evaluation based on fuzzy entropy is used to calculate the image definition value.
Step 1: size is applied to find the position with maximum image definition value.
We use the global search to find the optimal position denoted as ( {P}_m^1 ) with the maximum definition value.
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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