Logo image
Back
Contour-driven Sumi-e rendering of real photos
Journal article   Peer reviewed

Contour-driven Sumi-e rendering of real photos

X. Ning, H. Laga, S. Saito and M. Nakajima
Computers & Graphics, Vol.35(1), pp.122-134
2011
DOI: https://doi.org/10.1016/j.cag.2010.11.017
url
Link to Published Version *Subscription may be requiredView

Abstract

We propose a sketch-based system for rendering oriental brush strokes on complex shapes. While previous research has focused on methods for converting user-specified trajectories into oriental ink painting (Sumi-e) strokes, we propose an approach that takes as input the contours of complex shapes, estimates automatically the sizes of the brush footprints, their orientations, and the brush trajectory. Then, it renders them into oriental ink paintings. The approach performs in three steps; first complex shapes are decomposed into elementary shapes that can be rendered with a single brush stroke. Second, the brush trajectory estimation is formulated as the minimization of an energy function that measures the quality of the trajectory. Finally, the estimated trajectories are rendered into brush strokes by mapping on them footprint textures scanned from real images. We demonstrate that the proposed contour-driven approach is particularly suitable for converting real images into Sumi-e paintings with minimum interaction. To this end we develop a system where images are either interactively or automatically segmented into elementary shapes and converted into Sumi-e paintings. For the automatic segmentation, we observe that the strokes in Sumi-e paintings are nearly parallel to the edge tangent flow (ETF). We propose a segmentation procedure that is based on clustering the curves obtained by integrating the ETF of real images. Experiments on complex shapes show that the proposed contour-based approach produces a large variety of complex strokes compared to trajectory-based approaches.

Details

Metrics

26 Record Views
13 Times Cited - Web of Science

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Collaboration types
Domestic collaboration
International collaboration
Citation topics
7 Engineering & Materials Science
7.227 Manufacturing
7.227.1221 Spline Interpolation
Web Of Science research areas
Computer Science, Software Engineering
ESI research areas
Computer Science
Logo image