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Glass Patterns and Artistic Imaging

  • Giuseppe Papari
  • Nicolai Petkov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5414)

Abstract

The theory of Glass patterns naturally combines three essential aspects of painterly artworks: perception, randomness, and geometric structure. Therefore, it seems a suitable framework for the development of mathematical models of the visual properties that distinguish paintings from photographic images. With this contribution, we introduce a simple mathematical operator which transfers the microstructure of a Glass pattern to an input image, and we show that its output is perceptually similar to a painting. An efficient implementation is presented. Unlike most of the existing techniques for unsupervised painterly rendering, the proposed approach does not introduce ’magic numbers’ and has a nice and compact mathematical description, which makes it suitable for further theoretical analysis. Experimental results on a broad range of input images validate the effectiveness of the proposed method in terms of lack of undesired artifacts, which are present with other existing methods, and easy interpretability of the input parameters.

Keywords

Input Image Object Contour Isotropic Scaling Brush Stroke Glass Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Giuseppe Papari
    • 1
  • Nicolai Petkov
    • 1
  1. 1.Institute of Mathematics and Computing ScienceUniversity of GroningenNetherlands

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