2018
DOI: 10.1155/2018/3670498
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Field Coupling-Based Image Filter for Sand Painting Stylization

Abstract: Sand painting is a very new and popular performance art technique, and it has important meaning for the artistic creation. Computer-aided sand painting is now in the ascendant, whose fully automated mechanism is still an attractive challenge. Image data field, inspired from short-range nuclear force in physics, is introduced into sand painting stylization. With preserving edges and features, a fully automatic image filter pipeline based on image field coupling is proposed, which consists of three main steps, i… Show more

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Cited by 5 publications
(4 citation statements)
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“…Peng et al [10] used hand-painting, images, and three-dimensional effects to create an elegant and long rhythm of virtual light, leaving viewers with a colorful imagination space [10]. Wu et al [11] used spray paint or volatile pigments as tools to create graffiti on abandoned walls, car bodies, bridge opening, passages, and telephone poles [11].…”
Section: E Development Of Illustration Designmentioning
confidence: 99%
“…Peng et al [10] used hand-painting, images, and three-dimensional effects to create an elegant and long rhythm of virtual light, leaving viewers with a colorful imagination space [10]. Wu et al [11] used spray paint or volatile pigments as tools to create graffiti on abandoned walls, car bodies, bridge opening, passages, and telephone poles [11].…”
Section: E Development Of Illustration Designmentioning
confidence: 99%
“…To date, these benchmark datasets have been used in a variety of ways: to include some stylisation results from examples taken from the benchmark [4,[13][14][15][16]; to systematically test the performance of stylisation algorithms [17][18][19]; to provide appropriate test data as part of the optimisation of preset parameters for post-processing filters in BeCasso, an interactive mobile iOS app for image stylisation [20]; and to provide a competitive and common set of test images for a research course on image processing for mobile applications [21].…”
Section: Benchmark Datasetsmentioning
confidence: 99%
“…To date, these benchmark datasets have been used in a variety of ways: to include some stylisation results from examples taken from the benchmark [16], [17], [18], [19]; to provide appropriate test data as part of the optimisation of preset parameters for post-processing filters in BeCasso, an interactive mobile iOS app for image stylisation [20]; and to provide a competitive and common set of test images for a research course on image processing for mobile applications [21].…”
Section: Benchmark Datasetsmentioning
confidence: 99%