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Image inpainting based on image segmentation and segment classification

Authors: Eman T. Hassan; Hazem M. Abbas; Hoda K. Mohamed;

Image inpainting based on image segmentation and segment classification

Abstract

We present a new inpainting algorithm that is based on image segmentation and segment classification. First, we employ the mean shift algorithm to segment the input image. Then, we divide the original inpainting problem to be either one of the two problems: Large Segment Inpainting problem or Non-uniform Segments inpainting problem. The reason we do that is that human eye is more discerning to the errors in the structure and texture propagation of a large-uniform regions with less details while it is less discerning to errors in non-uniform regions with more details. We propose a novel algorithm for each one of the problems- Large Segment Inpainting and Non-uniform Segments inpainting- according to the main features of each one. The experimental results show the advantage of our technique which produces output images with better perceived visual quality.

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    popularity
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    influence
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Average
Average
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