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Several methods have been proposed by improving filling priorities and adding various foreground-background classification techniques in the exemplar methods including the methods in [18 23].
To overcome the limitations of these diffusion methods, exemplar methods combine both structural and textural properties when filling in missing areas [17].
In exemplar methods, a filling order is first computed on the hole boundary using the number of known pixels in a patch (confidence term) and structure in the neighborhood (data term).
Instead, we have chosen exemplar methods that use different kinds of data.
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Although the exemplar method has the advantage of recovering the structure, this priority order is not suitable for hole filling in the view synthesis context, since holes should be filled only from the background texture.
An exemplar method, pioneered by Nicholson, is described by Dumas et al., 2014 [2014
This suggests, again, the presentation of exemplars, methods, principles and modes as alternatives to be continually switched between rather than a hierarchy of use.
Our objective was to explore a variety of exemplar analysis methods from each of the different categories of analysis type in order to ascertain whether variation in the resulting trees is trivial or extensive, random or accompanied by systematic bias.
Exemplar-based methods have been used for the case of video completion in [32, 33].
Exemplar-based methods have been the most powerful techniques for dealing with large disoccluded regions.
Since the exemplar-based methods in[24] andirectlyrectly select known patches from the target image for inpainting, blurring tends to be reduced.
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