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Extraction of Affine Invariant Features Using Fractal

Jianwei Yang,Guosheng Cheng,Ming Li

Published 2013 in Advances in Mathematical Physics

ABSTRACT

An approach based on fractal is presented for extracting affine invariant features. Central projection transformation is employed to reduce the dimensionality of the original input pattern, and general contour (GC) of the pattern is derived. Affine invariant features cannot be extracted from GC directly due to shearing. To address this problem, a group of curves (which are called shift curves) are constructed from the obtained GC. Fractal dimensions of these curves can readily be computed and constitute a new feature vector for the original pattern. The derived feature vector is used in question for pattern recognition. Several experiments have been conducted to evaluate the performance of the proposed method. Experimental results show that the proposed method can be used for object classification.

PUBLICATION RECORD

  • Publication year

    2013

  • Venue

    Advances in Mathematical Physics

  • Publication date

    2013-05-15

  • Fields of study

    Mathematics, Computer Science

  • Identifiers
    DOI 10.1155/2013/950289
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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  • No concepts are published for this paper.

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