Journal of Mathematical Imaging and Vision

, Volume 57, Issue 1, pp 117–133 | Cite as

Fourier Descriptors Based on the Structure of the Human Primary Visual Cortex with Applications to Object Recognition

  • Amine Bohi
  • Dario Prandi
  • Vincente Guis
  • Frédéric Bouchara
  • Jean-Paul Gauthier


In this paper, we propose a supervised object recognition method using new global features and inspired by the model of the human primary visual cortex V1 as the semidiscrete roto-translation group \(SE(2,N) = {\mathbb {Z}}_N\rtimes {\mathbb {R}}^2\). The proposed technique is based on generalized Fourier descriptors on the latter group, which are invariant to natural geometric transformations (rotations, translations). These descriptors are then used to feed an SVM classifier. We have tested our method against the COIL-100 image database and the ORL face database, and compared it with other techniques based on traditional descriptors, global and local. The obtained results have shown that our approach looks extremely efficient and stable to noise, in presence of which it outperforms the other techniques analyzed in the paper.


Descriptor Fourier transform Hexagonal grid Geometric transformations Support vector machine Object recognition 



This research has been supported by the European Research Council, ERC StG 2009 “GeCoMethods”, contract n. 239748. The second and last authors were partially supported by the Grant ANR-15-CE40-0018 of the ANR.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Amine Bohi
    • 1
  • Dario Prandi
    • 2
  • Vincente Guis
    • 1
  • Frédéric Bouchara
    • 1
  • Jean-Paul Gauthier
    • 1
  1. 1.LSIS LaboratoryUniversity of South Toulon - VarLa Garde CedexFrance
  2. 2.CEREMADE LaboratoryUniversity of Paris-DauphineParis Cedex 16France

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