Feed Forward Genetic Image Network: Toward Efficient Automatic Construction of Image Processing Algorithm

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Shinichi Shirakawa and Tomoharu Nagao",
  title =        "Feed Forward Genetic Image Network: Toward Efficient
                 Automatic Construction of Image Processing Algorithm",
  booktitle =    "Advances in Visual Computing: Proceedings of the 3rd
                 International Symposium on Visual Computing (ISVC 2007)
                 Part II",
  year =         "2007",
  volume =       "4842",
  series =       "Lecture Notes in Computer Science",
  pages =        "287--297",
  address =      "Lake Tahoe, Nevada, USA",
  month =        nov # " 26-28",
  publisher =    "Springer",
  editor =       "George Bebis and Richard Boyle and Bahram Parvin and 
                 Darko Koracin and Nikos Paragios and 
                 Syeda-Mahmood Tanveer and Tao Ju and Zicheng Liu and 
                 Sabine Coquillart and Carolina Cruz-Neira and 
                 Torsten Muller and Tom Malzbender",
  publisher_address = "Berlin / Heidelberg",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-540-76855-5",
  URL =          "http://www.springerlink.com/content/875l8257231732pq/",
  DOI =          "doi:10.1007/978-3-540-76856-2_28",
  size =         "11 pages",
  abstract =     "A new method for automatic construction of image
                 transformation, Feed Forward Genetic Image Network
                 (FFGIN), is proposed in this paper. FFGIN evolves feed
                 forward network structured image transformation
                 automatically. Therefore, it is possible to
                 straightforward execution of network structured image
                 transformation. The genotype in FFGIN is a fixed length
                 representation and consists of string which encode the
                 image processing filter ID and connections of each node
                 in the network. In order to verify the effectiveness of
                 FFGIN, we apply FFGIN to the problem of automatic
                 construction of image transformation which is pasta
                 segmentation and compare with several method. From the
                 experimental results, it is verified that FFGIN
                 automatically constructs image transformation.
                 Additionally, obtained structure by FFGIN is unique,
                 and reuses the transformed images.",
  notes =        "Also known as \cite{bb53106} From Annotated Computer
                 Vision Bibliography",

Genetic Programming entries for Shinichi Shirakawa Tomoharu Nagao