Topological data analysis to improve exemplar-based inpainting

Al-Jaberi, Ahmed A and Assad, A. and Jassim, Sabah A. and Al-Jawad, Naseer (2018) Topological data analysis to improve exemplar-based inpainting. In: SPIE Commercial + Scientific Sensing and Imaging, 15-19 April 2018, Orlando, Florida, United States.

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Abstract

Image inpainting is the process of filling in the missing region to preserve continuity of its overall content and semantic. In this paper, we present a novel approach to improve an existing scheme, called exemplar-based inpainting algorithm, using Topological Data Analysis (TDA). TDA is a mathematical approach concern studying shapes or objects to gain information about connectivity and closeness property of those objects. The challenge in using exemplar-based inpainting is that missing regions neighborhood area needs to have a relatively simple texture and structure. We studied the topological properties (e.g. number of connected components) of missing regions surrounding the missing area by building a sequence of simplicial complexes (known as persistent homology) based on a selected group of uniform Local binary Pattern LBP. Connected components of image regions generated by certain landmark pixels, at different thresholds, automatically quantify the texture nature of the missing regions surrounding areas. Such quantification help determine the appropriate size of patch propagation. We have modified the patch propagation priority function using geometrical properties of curvature of isophote and improved the matching criteria of patches by calculating the correlation coefficients from spatial, gradient and Laplacian domain. We use several image quality measures to illustrate the performance of our approach in comparison to similar inpainting algorithms. In particular, we shall illustrate that our proposed scheme outperforms the state-of-the-art exemplar-based inpainting algorithms

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: TDA, Exemplar-based inpainting, Normalized Correlation Coefficient (NCC), Curvature operator, Priority function.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: School of Science > School of Computing
Depositing User: Rachel Pollard
Date Deposited: 23 Aug 2019 10:17
Last Modified: 23 Aug 2019 10:17
URI: http://bear.buckingham.ac.uk/id/eprint/404

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