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This is done in order to minimize image distortion imposed by inversely normalizing the normalized image to obtain the original.
However, by doing so, we would afterwards have to inversely normalize the watermarked normalized image to obtain the watermarked original image so that the watermark embedding process would be complete.
This is done to avoid the overhead of inversely normalizing the watermark, since the normalized image is already available.
– D: add 1 to all the pixels then normalize as in S. – A: normalize as in S and save the normalized image as a sed file.
The detection process consists of normalizing the input image and extracting the feature points of the normalized image, after which a correlation detector is employed to detect the possibly inserted watermark in the normalized image.
To obtain correct feature activations with feature extractors learned from whitened image patches, we should normalize each image patch in each image to zero mean and multiply each normalized image patch x i by W ZCAwhite for each activation: {a}_i=sigma left(W{W}_{ZCAwhite}{x}_i+bright) (8 where W denotes the feature learned by a sparse autoencoder.
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This is achieved via computing the edge gradient magnitude of the illumination-normalized image.
We apply the abovementioned GRAB operator on a geo-normalized image as shown in Figure 2.
Let the grayscale version of the illumination-normalized image Ψ k (c) be denoted as Ψ(c).
In our study, statistical features are computed by sliding an overlapped window over the height-normalized image of each ligature.
The image is cropped based on the outline and different algorithms are applied using a size-normalized image.
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