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Again, the matching is basically executed by comparing the luminance values of pixels covering the effect in the template frame.
After the template frame is constructed, detecting all the transition effects for locating slow-motion replays can be done effectively.
The more processing units are considered when constructing the template, the better quality the template frame will be and the more execution time will be expected.
A template frame is therefore represented as x t → m 1 x t ⋮ m n x t w 1 x t ⋮ w n x t. (1).
The LLR distance contrasts the fit score of a test frame with its best model against its fit score with the best model of the template frame, and it therefore compares the two frames indirectly through the models.
The frame with the largest number of matched pixels is selected as the template frame, and the luminance mean at these matched positions in the units will be calculated to form the template.
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Figure 7 Two examples of template frames from the videos.
The extracted template frames, along with the corresponding video frames, are demonstrated in Figure 11.
Two examples are shown in Figure 7, including the constructed template frames and the associated video frames.
When the template frames are represented by GMM indices, the Euclidean and Mahalanobis distances are no longer suitable.
In general, n < < d, and hence storing the template frames in GMM indices requires a much smaller space than storing the feature frames for the Templates.
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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