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The first option is to use the feature values from a video's frames.
Therefore, we only use the feature values that are salient because they appear at few positions.
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The embedding algorithm uses the feature values selected in step 3 as input.
For a given feature, the inter-reader stability was defined as the intraclass correlation coefficient (ICC) computed using the feature values obtained through all readers for all cases.
Figure 2 illustrates the process of training and classifying SOM using the feature values extracted in "Feature extraction for art paintings" section.
The mean, μ, and standard deviation, σ, are computed for the magnitude of each filter response and these values are used as the feature values.
The encoded information became the lexicon occurrence feature, and the represented feature value corresponding to the current input token was used as the feature value.
The search for these correspondences can be realized algorithmically in an efficient way using sorted tables of the feature values of both input frames.
Learning a classifier requires labeled training data, which describes a set of subjects using, for each subject, the feature values x i, along with a label y i (in our case, "Colonoscopy" or "Normal").
The magnitude is used here as one of the feature values, that is, J r I ( x I ) = Ĵ r I ( x I ).
Thereafter, the integral image uses seven operations to add the feature values for the weak classifier when the weak classifier is a two-rectangle feature, which entails the least number of operations.
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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