A Genetic Programming Approach to the Design of Interest Point Operators

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Gustavo Olague and Leonardo Trujillo",
  title =        "A Genetic Programming Approach to the Design of
                 Interest Point Operators",
  booktitle =    "Bio-Inspired Hybrid Intelligent Systems for Image
                 Analysis and Pattern Recognition",
  publisher =    "Springer",
  year =         "2009",
  editor =       "Patricia Melin and Janusz Kacprzyk and 
                 Witold Pedrycz",
  volume =       "256",
  series =       "Studies in Computational Intelligence",
  chapter =      "3",
  pages =        "49--65",
  keywords =     "genetic algorithms, genetic programming, computer
  isbn13 =       "978-3-642-04515-8",
  DOI =          "doi:10.1007/978-3-642-04516-5_3",
  abstract =     "Recently, the detection of local image feature has
                 become an indispensable process for many image analysis
                 or computer vision systems. In this chapter, we discuss
                 how Genetic Programming (GP), a form of evolutionary
                 search, can be used to automatically synthesise image
                 operators that detect such features on digital images.
                 The experimental results we review, confirm that
                 artificial evolution can produce solutions that
                 outperform many man-made designs. Moreover, we argue
                 that GP is able to discover, and reuse, small code
                 fragments, or building blocks, that facilitate the
                 synthesis of image operators for point detection.
                 Another noteworthy result is that the GP did not
                 produce operators that rely on the auto-correlation
                 matrix, a mathematical concept that some have
                 considered to be the most appropriate to solve the
                 point detection task. Hence, the GP generates operators
                 that are conceptually simple and can still achieve a
                 high performance on standard tests.",

Genetic Programming entries for Gustavo Olague Leonardo Trujillo