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The upshot is a set of feature maps, one for each filter, showing which patches of the original image contain the sought-after element.
Compression is achieved by projecting the tasks' displays on a small set of feature maps.
Similarly, automatic transformations allow the derivation of an EvoFM from a given set of feature models.
Therefore, for region a set of feature vectors is obtained,, where is the number of blocks.
False matches are removed from the initial set of feature point matches.
Weak classifiers are linear functions that are learned from a set of feature vectors.
Within an iteration, we first obtain a projective transformation from the current set of feature pairs.
A set of feature points are extracted and they are categorized into inliers and outliers.
For each group-to-group matching operation, we obtain an initial set of feature point matches.
where X l is the set of feature vectors corresponding to a training video clip.
The set of feature candidates consists of 50 items, numbered between ℱ1 and ℱ50.
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CEO of Professional Science Editing for Scientists @ prosciediting.com