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Exact(9)
The difference images between the input image and the reconstructed image are presented in (c), (e), (g), and (i).
The amount of I 1 indicates the similarity between the input image and the stored image (#1).
Such processors are designed based on cross correlation in the frequency domain between the input image and the input weights of neural networks.
This observation intuitively corresponds to the fact that learning shared weights improves the inner relation between the input image and the baseline.
The average absolute discrete entropy difference DEave between the input image X i and the output image Y i is defined as DE ave = 1 n ∑ i = 1 n DE X i - DE Y i. (19).
(1 - 3), the output intensity can be gotten by convolution between the input image and the Gaussian function, the process can be represented as: {I}_cleft x,yright)={I}_{blur}^0{left x,yright)}^{ast }gleft x,yright) (16).
Similar(51)
The methods in this paper are designed to improve the accuracy of a features-based face recognition system when the pose between the input images and training images are different.
Camera poses are estimated from matched feature points between map points and the input image.
To achieve this goal, the relation (or mapping function) between the gradient of the input image and the desired solution should be non-linear.
Although this method has increased the contrast between different regions of the input image, the contrast within each region of the image is considerably reduced.
Similarly, I 9 means the similarity between the stored image (#9) and the input image.
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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