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Exploiting the synergy between fractal dimension and lacunarity for improved texture recognition. (Kilic, Kemal Ihsan.)
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Exploiting the synergy between fractal dimension and lacunarity for improved texture recognition.
Author:
Kilic, Kemal Ihsan. Search Author in Amazon Books

Publisher:
Elsevier B.V.,
Edition:
2011.
Classification:
QA614.86
URL:

http://library.neu.edu.tr:2048/login?url=http://dx.doi.org/10.1016/j.sigpro.2011.04.018
Detailed notes
    - Fractal dimension measures the geometrical complexity of images. Lacunarity being a measure of spatial heterogeneity can be used to differentiate between images that have similar fractal dimensions but different appearances. This paper presents a method to combine fractal dimension (FD) and lacunarity for better texture recognition. For the estimation of the fractal dimension an improved algorithm is presented. This algorithm uses new box-counting measure based on the statistical distribution of the gray levels of the “boxes”. Also for the lacunarity estimation, new and faster gliding-box method is proposed, which utilizes summed area tables and Levenberg–Marquardt method. Methods are tested using Brodatz texture database (complete set), a subset of the Oulu rotation invariant texture database (Brodatz subset), and UIUC texture database (partial). Results from the tests showed that combining fractal dimension and lacunarity can improve recognition of textures.
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Library
Section
EOL-8
Item available
NEU LibraryOnline (QA614.86 .E97 2011)
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