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Our technique uses the Kolmogorov-Smirnov test [29] as a metric to evaluate the similarity between the two histograms.
In the specific case study of this work, the difference between the two histograms of grey levels of the reference and moving images is smaller than 0.25 and the goodness of alignment cannot, therefore, be evaluated with the mutual information.
It is evident that the first histogram exhibits the characteristic pattern of a distribution of coefficients that have undergone a double compression, whereas in the second one, these artifacts have been removed, and thus the difference between the two histograms is significant.
where H p and H q represent the HOAOF of local patch centered at the pixel p and q in frame I, respectively, D H p,H q ) is χ2 distance between the two histograms: D ( H p, H q ) = 1 2 ∑ k ( H p ( k ) − H q ( k ) ) 2 H p ( k ) + H q ( k ) (10).
If a model of visual saliency predicts human gaze significantly better than chance, the saliency measure computed at human fixations should be higher than that computed at random locations, leading to a high KL divergence between the two histograms.
We also measured the difference by computing the overlap between the two histograms.
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No tangible difference between bad and good probe sets is seen when comparing the two histograms shown in Figure 1.
The orientation histogram of one image is cyclically slided over the other orientation histogram from the other image, and the position where the two histograms best match is used to determine the rotational difference between the images, assuming H A and H B are the magnitudes weighted orientation histograms of the reference image and the moving images, respectively.
Compare the two histograms.
The two histograms are shown in Fig. 10.
Subsequently, the two histograms were normalized for further histogram matching to eliminate the influence of scale differences between images.
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