Estimating Color and Texture Parameters for Vector Graphics

Information

• Publication Type: Invited Talk
• Workgroup(s)/Project(s):
  • Detailed Surfaces for Interactive Rendering
• Date: 2011
• Event: Academy of Sciences of the Czech Republic in Prague
• Location: Prague (Czech Republic)
• Conference date: 30. June 2011

Abstract

Diffusion curves are a powerful vector graphic representation that stores an image as a set of 2D Bezier curves with colors defined on either side. These colors are diffused over the image plane, resulting in smooth color regions as well as sharp boundaries. In this paper, we introduce a new automatic diffusion curve coloring algorithm. We start by defining a geometric heuristic for the maximum density of color control points along the image curves. Following this, we present a new algorithm to set the colors of these points so that the resulting diffused image is as close as possible to a source image in a least squares sense. We compare our coloring solution to the existing one which fails for textured regions, small features, and inaccurately placed curves. The second contribution of the paper is to extend the diffusion curve representation to include texture details based on Gabor noise. Like the curves themselves, the defined texture is resolution independent, and represented compactly. We define methods to automatically make an initial guess for the noise texure, and we provide intuitive manual controls to edit the parameters of the Gabor noise. Finally, we show that the diffusion curve representation itself extends to storing any number of attributes in an image, and we demonstrate this functionality with image stippling an hatching applications.

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BibTeX

@talk{jeschke-2011-talkPrague,
  title =      "Estimating Color and Texture Parameters for Vector Graphics",
  author =     "Stefan Jeschke",
  year =       "2011",
  abstract =   "Diffusion curves are a powerful vector graphic
               representation that stores an image as a set of 2D Bezier
               curves with colors defined on either side. These colors are
               diffused over the image plane, resulting in smooth color
               regions as well as sharp boundaries. In this paper, we
               introduce a new automatic diffusion curve coloring
               algorithm. We start by defining a geometric heuristic for
               the maximum density of color control points along the image
               curves. Following this, we present a new algorithm to set
               the colors of these points so that the resulting diffused
               image is as close as possible to a source image in a least
               squares sense. We compare our coloring solution to the
               existing one which fails for textured regions, small
               features, and inaccurately placed curves. The second
               contribution of the paper is to extend the diffusion curve
               representation to include texture details based on Gabor
               noise. Like the curves themselves, the defined texture is
               resolution independent, and represented compactly. We define
               methods to automatically make an initial guess for the noise
               texure, and we provide intuitive manual controls to edit the
               parameters of the Gabor noise. Finally, we show that the
               diffusion curve representation itself extends to storing any
               number of attributes in an image, and we demonstrate this
               functionality with image stippling an hatching applications.",
  event =      "Academy of Sciences of the Czech Republic in Prague",
  location =   "Prague (Czech Republic)",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2011/jeschke-2011-talkPrague/",
}