@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},
}