Delaunay Painting: Perceptual Image Colouring from Raster Contours with Gaps
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Date
2022
Journal Title
Journal ISSN
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Publisher
© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd.
Abstract
We introduce Delaunay Painting, a novel and easy‐to‐use method to flat‐colour contour‐sketches with gaps. Starting from a Delaunay triangulation of the input contours, triangles are iteratively filled with the appropriate colours, thanks to the dynamic update of flow values calculated from colour hints. Aesthetic finish is then achieved, through energy minimisation of contour‐curves and further heuristics enforcing the appropriate sharp corners. To be more efficient, the user can also make use of our colour diffusion framework, which automatically extends colouring to small, internal regions such as those delimited by hatches. The resulting method robustly handles input contours with strong gaps. As an interactive tool, it minimizes user's efforts and enables any colouring strategy, as the result does not depend on the order of interactions. We also provide an automatized version of the colouring strategy for quick segmentation of contours images, that we illustrate with applications to medical imaging and sketch segmentation.
Description
@article{10.1111:cgf.14517,
journal = {Computer Graphics Forum},
title = {{Delaunay Painting: Perceptual Image Colouring from Raster Contours with Gaps}},
author = {Parakkat, Amal Dev and Memari, Pooran and Cani, Marie‐Paule},
year = {2022},
publisher = {© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.14517}
}