Image Registration of Very Large Images via Genetic Programming

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

  author =       "Sarit Chicotay and Omid E. David and 
                 Nathan S. Netanyahu",
  booktitle =    "IEEE Conference on Computer Vision and Pattern
                 Recognition Workshops (CVPRW 2014)",
  title =        "Image Registration of Very Large Images via Genetic
  year =         "2014",
  month =        jun,
  pages =        "329--334",
  abstract =     "Image registration (IR) is a fundamental task in image
                 processing for matching two or more images of the same
                 scene taken at different times, from different
                 viewpoints and/or by different sensors. Due to the
                 enormous diversity of IR applications, automatic IR
                 remains a challenging problem to this day. A wide range
                 of techniques has been developed for various data types
                 and problems. These techniques might not handle
                 effectively very large images, which give rise usually
                 to more complex transformations, e.g., deformations and
                 various other distortions.

                 In this paper we present a genetic programming (GP)
                 based approach for IR, which could offer a significant
                 advantage in dealing with very large images, as it does
                 not make any prior assumptions about the transformation
                 model. Thus, by incorporating certain generic building
                 blocks into the proposed GP framework, we hope to
                 realize a large set of specialised transformations that
                 should yield accurate registration of very large
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CVPRW.2014.56",
  notes =        "Also known as \cite{6910002}",

Genetic Programming entries for Sarit Chicotay Omid E David Nathan S Netanyahu