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Specifically, for each image of the training set, the pixels belonging to red-eye artifacts have been labeled as red-eye pixels (REP), whereas the surrounding pixels within windows of fixed size have been labeled as non-red-eye pixels (NREP).
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The image of the train about to leave -- perhaps the last train -- also made me think that better ones than I had to go.
The exhibition, below the theater, opens with an image of the train that brought Lincoln to Washington for his inauguration in 1860 and then presents a deft history of the Civil War and Lincoln's life in the White House.
Von Berg's ruse is discovered, however, and we hear the guards shooting at Leduc as we see, above the stage, a projected image of the train that will no doubt carry Von Berg to the camps and to his death.
The ad created in viewers a vivid, multisensory network of associations - associations not just with the word hope but to the image of Hope in small-town America in an era gone by, captured by the image of the train station, and the sound of hope, captured in his voice.
Each image of the train and test set transformed to feature vector of length 356.
If cinema is illusion, and from its birth it is linked to magic, then the visual and sensory experience Alfonso and Emmanuel "El Chivo" Lubezki achieved in the first 30 minutes of Gravity equate to that first image of the train by the Lumiere brothers that so greatly impacted the first audience in the theater, even making them flee.
The values of the moments M pq are computed using (13) for each of the images in the face database, whereas the ICCs for each of the moments are estimated using (19 21) only for the images of the training set.
The first step is to calculate the covariance matrix G t of fractal features which is obtained from the images of the training database as follows: G t = 1 M ∑ ( X j - X ^ ) T * ( X j - X ^ ), (2).
First, transform encrypted images of the training set to feature vectors and then train w SVM models by performing multi-class SVM, where the one-against-all strategy is adopted, i.e., when training the ith SVM model, all images belonging to the ith category are labeled positive and the labels of the remaining images are set negative.
For a target image, its annotation is predicted based on a mutual similarity of the target image to the training images.
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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