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[CH+06]  Salience-Adaptive Painterly Rendering using Genetic Search

Collomosse:2006:SAP (Article)
Author(s)Collomosse J. and Hall P.
Title« Salience-Adaptive Painterly Rendering using Genetic Search »
JournalInternational Journal on Artificial Intelligence Tools
Volume15
Number4
Page(s)551--575
Year2006

Abstract
We present a new non-photorealistic rendering (NPR) algorithm for rendering photographs in an impasto painterly style. We observe that most existing image-based NPR algorithms operate in a spatially local manner, typically as non-linear image filters seeking to preserve edges and other high-frequency content. By contrast, we argue that figurative artworks are salience maps, and develop a novel painting algorithm that uses a genetic algorithm (GA) to search the space of possible paintings for a given image, so approaching an ``optimal'' artwork in which salient detail is conserved and non-salient detail is attenuated. Differential rendering styles are also possible by varying stroke style according to the classification of salient artifacts encountered, for example edges or ridges. We demonstrate the results of our technique on a wide range of images, illustrating both the improved control over level of detail due to our salience adaptive painting approach, and the benefits gained by subsequent relaxation of the painting using the GA.

BibTeX code
@article{Collomosse:2006:SAP,
  optpostscript = {},
  number = {4},
  month = aug,
  author = {John P. Collomosse and Peter M. Hall},
  optkey = {},
  optannote = {},
  localfile = {papers/Collomosse.2006.SAP.pdf},
  optkeywords = {},
  doi = {http://dx.doi.org/10.1142/S0218213006002813},
  optciteseer = {},
  journal = {International Journal on Artificial Intelligence Tools},
  opturl = {},
  volume = {15},
  optwww = {},
  title = {{S}alience-{A}daptive {P}ainterly {R}endering using {G}enetic
           {S}earch},
  abstract = {We present a new non-photorealistic rendering (NPR) algorithm for
              rendering photographs in an impasto painterly style. We observe
              that most existing image-based NPR algorithms operate in a
              spatially local manner, typically as non-linear image filters
              seeking to preserve edges and other high-frequency content. By
              contrast, we argue that figurative artworks are salience maps, and
              develop a novel painting algorithm that uses a genetic algorithm
              (GA) to search the space of possible paintings for a given image,
              so approaching an ``optimal'' artwork in which salient detail is
              conserved and non-salient detail is attenuated. Differential
              rendering styles are also possible by varying stroke style
              according to the classification of salient artifacts encountered,
              for example edges or ridges. We demonstrate the results of our
              technique on a wide range of images, illustrating both the
              improved control over level of detail due to our salience adaptive
              painting approach, and the benefits gained by subsequent
              relaxation of the painting using the GA.},
  pages = {551--575},
  year = {2006},
}

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