Detecting Scale-Invariant Regions Using Evolved Image Operators

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InCollection{Trujillo:2009:EIASP,
  author =       "Leonardo Trujillo and Gustavo Olague",
  title =        "Detecting Scale-Invariant Regions Using Evolved Image
                 Operators",
  booktitle =    "Evolutionary Image Analysis and Signal Processing",
  publisher =    "Springer",
  year =         "2009",
  editor =       "Stefano Cagnoni",
  volume =       "213",
  series =       "Studies in Computational Intelligence",
  pages =        "21--40",
  address =      "Berlin / Heidelberg",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-01635-6",
  ISSN =         "1860-949X",
  DOI =          "doi:10.1007/978-3-642-01636-3_2",
  abstract =     "This chapter describes scale-invariant region
                 detectors that are based on image operators synthesised
                 through Genetic Programming (GP). Interesting or
                 salient regions on an image are of considerable
                 usefulness within a broad range of vision problems,
                 including, but not limited to, stereo vision, object
                 detection and recognition, image registration and
                 content-based image retrieval. A GP-based framework is
                 described where candidate image operators are
                 synthesized by employing a fitness measure that
                 promotes the detection of stable and dispersed image
                 features, both of which are highly desirable
                 properties. After a significant number of experimental
                 runs, a plateau of maxima was identified within the
                 search space that contained operators that are similar,
                 in structure and/or functionality, to basic LoG or DoG
                 filters. Two such operators with the simplest structure
                 were selected and embedded within a linear scale space,
                 thereby making scale-invariant feature detection a
                 straightforward task. The proposed scale-invariant
                 detectors exhibit a high performance on standard tests
                 when compared with state-of-the-art techniques. The
                 experimental results exhibit the ability of GP to
                 construct highly reusable code for a well known and
                 hard task when an appropriate optimisation problem is
                 framed.",
  notes =        "EvoISAP, EvoNET, EvoStar",
}

Genetic Programming entries for Leonardo Trujillo Gustavo Olague

Citations