Fast polar and spherical fourier descriptors for feature extraction

Zhuo Yang*, Sei Ichiro Kamata

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Polar Fourier Descriptor(PFD) and Spherical Fourier Descriptor( SFD) are rotation invariant feature descriptors for two dimensional( 2D) and three dimensional(3D) image retrieval and pattern recognition tasks. They are demonstrated to show superiorities compared with other methods on describing rotation invariant features of 2D and 3D images. However in order to increase the computation speed, fast computation method is needed especially for machine vision applications like realtime systems, limited computing environments and large image databases. This paper presents fast computation method for PFD and SFD that are deduced based on mathematical properties of trigonometric functions and associated Legendre polynomials. Proposed fast PFD and SFD are 8 and 16 times faster than direct calculation that significantly boost computation process. Furthermore, the proposed methods are also compact for memory requirements for storing PFD and SFD basis in lookup tables. The experimental results on both synthetic and real data are given to illustrate the efficiency of the proposed method.

Original languageEnglish
Pages (from-to)1708-1715
Number of pages8
JournalIEICE Transactions on Information and Systems
Issue number7
Publication statusPublished - 2010 Jul


  • Fast algorithm
  • Image retrieval
  • Polar fourier descriptor
  • Rotation invariant
  • Spherical fourier descriptor

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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