Algorithm Discovery Using the Genetic Programming Paradigm: Extracting Low-Contrast Curvilinear Features from SAR Images of Arctic Ice

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InCollection{daida:1996:aigp2,

• author = "Jason M. Daida and Jonathan D. Hommes and Tommaso F. Bersano-Begey and Steven J. Ross and John F. Vesecky",
• title = "Algorithm Discovery Using the Genetic Programming Paradigm: Extracting Low-Contrast Curvilinear Features from {SAR} Images of Arctic Ice",
• booktitle = "Advances in Genetic Programming 2",
• publisher = "MIT Press",
• year = "1996",
• editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}",
• pages = "417--442",
• chapter = "21",
• address = "Cambridge, MA, USA",
• keywords = "genetic algorithms, genetic programming, GAIA",
• ISBN = "0-262-01158-1",
• URL = "http://sitemaker.umich.edu/daida/files/GP2_cha21.pdf",
• URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277526",
• DOI = "doi:10.7551/mitpress/1109.003.0028",
• size = "26 pages",
• abstract = "We discuss the application of genetic programming (GP) to image analysis problems in geoscience and remote sensing and describes how a GP can be adapted for processing large data sets (in our case, 1024 x 1024 pixel images plus texture channels). The featured problem is one that has not been adequately solved for this type of imagery. We describe the placement of GP in the overall scheme of algorithm discovery in geoscience image analysis and describe how GP complements a scientist's hypothesis-test derivation of such algorithms. The featured solution consists of a standard non-ADF GP that incorporates a dynamic fitness function.",
• notes = "see also http://www.sprl.umich.edu/acers/gaia/aigpGaia.html",

}

Genetic Programming entries for Jason M Daida Jonathan D Hommes Tommaso F Bersano-Begey Steven J Ross John F Vesecky

Citations