Multiple illuminated paper textures for drawing strokes

Murakami, Kyoko; Tsuruno, Reiji; Genda, Etsuo

doi:10.1109/cgi.2005.1500408

Cited by 12 publications

(14 citation statements)

References 11 publications

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“…Furthermore, heavy deposition of low-viscosity pigment produces a smearing effect, wherein artists express smoothly varying tone or color in pastel paintings by rubbing the pigments deposited on the surface with their hands or soft tissues ( Fig. 2 (a) (6)).…”

Section: Pastel Media Analysismentioning

confidence: 99%

A Multi-Layered Framework for color pastel painting

2017

KSII TIIS

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We present a computerized framework for producing color pastel painting from the visual information extracted from a photograph. To express color pastel painting, we propose a multi-layered framework where each layer possesses pastel stroke patterns of different colors. The stroke patterns in the separate layers are merged by a rendering equation based on a participating media rendering scheme. To produce the stroke patterns in each layer, we review the physical properties of pastels and the mechanism of a convolution framework, which is the most widely used scheme to simulate stick-shaped media such as pencils. We devise the following computational models to extend the convolution framework to produce pastel strokes: a bold noise model, which mimics heavy and clustered deposition of pigment, and a thick convolution filter model, which produces various pastel stroke patterns. We also design a stochastic color coordination scheme to mimic pastel artists' color expression and to separate strokes in different layers. To demonstrate the soundness of approach, we conduct several experiments using the models and compare the results with existing works or real pastel paintings. We present the results for several pastel paintings to demonstrate the excellent performance of our framework.

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Section: Pastel Media Analysismentioning

confidence: 99%

A Multi-Layered Framework for color pastel painting

2017

KSII TIIS

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“…The color of each stroke is obtained by sampling the corresponding pixels in the original photograph. Murakami et al [MTG05] have simulated drawing materials such as charcoal, crayon and pencil with a stroke‐based scheme. The effect of a stroke depends on the paper texture which is illuminated in twelve different directions.…”

Section: Related Workmentioning

confidence: 99%

“…In Section 5.1 we contrast the edge tangent flow [YM11], RBF function [HE04], and image parsing [ZZXZ09] techniques which other authors have used to determine stroke direction with our own scheme. Color customization: Most existing pencil drawing schemes are restricted to monochrome. Others [YMI04a, MJN05, MTG05] can produce color, but cannot match the tones of an input photograph successfully. Our scheme samples colors from the input image and uses them to depict objects in detail and in a visually pleasing style.…”

Section: Introductionmentioning

confidence: 99%

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A Stylized Approach for Pencil Drawing from Photographs

Yang

Kwon

Min

2012

Computer Graphics Forum

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We present a stylized scheme that produces pencil drawings in a range of styles from an image. To produce controllable pencil drawing effects and remedy the problems of existing convolution‐based schemes, we develop a swing bilateral LIC (SBL) filter. Our first approach to express the styled pencil drawings is to control the directions of pencil strokes that depicts both shapes and smooth tone. Another approach is to produce colors of pencil drawings by sampling colors from real color pencils. The third approach is to mimic the artistic technique that increases the details of drawings in a progressive manner. We present drawings in several styles and compare some of them directly with illustrations taken from an artists' work.

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“…Based on the consideration of Murakami et al [2005], the brightness on the paper illuminated from slanted directions is used for pigment deposition. In this improved method, the brightness of each polygon is estimated by an ordinal diffuse calculation.…”

Section: Introductionmentioning

confidence: 99%