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Durham Research Online
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Example-based image recoloring in indoor environment.

Lin, Xianxuan and Wang, Xun and Li, Frederick W. B. and Li, Jinyu and Yang, Bailin and Zhang, Kaili and Wei, Tianxiang (2020) 'Example-based image recoloring in indoor environment.', Computer animation and virtual worlds., 31 (2). e1917.

Abstract

Color structure of a home scene image closely relates to the material properties of its local regions. Existing color migration methods typically fail to fully infer the correlation between the coloring of local home scene regions, leading to a local blur problem. In this paper, we propose a color migration framework for home scene images. It picks the coloring from a template image and transforms such coloring to a home scene image through a simple interaction. Our framework comprises three main parts. First, we carry out an interactive segmentation to divide an image into local regions and extract their corresponding colors. Second, we generate a matching color table by sampling the template image according to the color structure of the original home scene image. Finally, we transform colors from the matching color table to the target home scene image with the boundary transition maintained. Experimental results show that our method can effectively transform the coloring of a scene matching with the color composition of a given natural or interior scenery.

Item Type:Article
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1002/cav.1917
Publisher statement:This is the accepted version of the following article: Lin, Xianxuan, Wang, Xun, Li, Frederick W. B., Li, Jinyu, Yang, Bailin, Zhang, Kaili & Wei, Tianxiang (2020). Example-based Image Recoloring in Indoor Environment. Computer Animation and Virtual Worlds 31(2): e1917 which has been published in final form at https://doi.org/10.1002/cav.1917. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for self-archiving.
Date accepted:15 October 2019
Date deposited:08 November 2019
Date of first online publication:09 December 2019
Date first made open access:09 December 2020

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