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Gradient histogram statistics.
2. Gradient histogram statistics .
Levin fits the observed gradient histogram using a mixture model.
In their work, specific NSS features drawn from the gradient histogram were used.
Liu et al. used the gradient histogram span as a feature in their classification model.
For this, we adopt the idea of gradient histogram preservation (GHP) in [12], which is to impose a constraint that the processed image has the same gradient histogram as the estimated original one.
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The gradient histograms have been computed from the x-gradient images.
For region description, SIFT uses gradient histograms in 16 subspaces around the point of interest.
The features represent 8-bin gradient histograms distributed in the local cells of 4 × 4 depth map.
After calculating the gradient map for each image, SIFT creates oriented gradient histograms for 4 × 4 grid regions, instead of 2 × 2 as in HOG.
DAISY is essentially similar to SIFT, except that is uses a Gaussian kernel to aggregate the gradient histograms in different bins whereas SIFT relies on a triangular shaped kernel.
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