Human artists often do the design of art patterns manually. The designer first imagines the art patterns in his mind, and then draws them on paper or canvas. This process is repeated until the desirable art patterns are created. It is time consuming and the novelty of the resulting art patterns is limited to the human’s imagination, which is difficult to meet the requirements for a huge amount of art patterns in a manufacturing industry such as textile. This chapter will mainly discuss how to let the computer assist in the generation of the art patterns, including:
  1. (1)

    Art pattern creation by fractals. It is based on the principles of fractal geometry, and performs the numerical calculation by the iterative function system. The output of the numerical data are colored to generate the resultant art patterns.

  2. (2)

    Art pattern creation by shape grammars. Shape grammars specify a mechanism for selecting and performing recursive rules for shape computations. The computer handles the representation and computation of shapes, rules, and the presentation of correct design alternatives. This frees the designer to specify, explore, develop design languages in terms of the shape grammar, and select alternatives for the desirable art patterns.

  3. (3)

    Layout-based creation of art patterns. Three basic layout-based art pattern design methods are presented: (a) How to convert the graphical layout into the resulting art patterns in terms of the specified structure of craftwork. (b) How to create an aesthetic layout of an art pattern by the regular layout. (c) How to automatically/semi-automatically place the user specified graphical entities by a specific artistic style.

  4. (4)

    Knowledge-based creation of art patterns. The artificial intelligence techniques are employed to represent the design knowledge and aesthetic conventions of art patterns, and then generate the resultant art patterns by reasoning on them.



Primitive Element Iterate Function System Regular Layout Shape Grammar Growth Rule 
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

© Zhejiang University Press, Hangzhou and Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Weidong Geng
    • 1
  1. 1.State Key Lab of CAD&CG Digital Media Technology Department College of Computer ScienceZhejiang UniversityHangzhouChina

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