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

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1507.06617.pdf (477.8Kb)
Date
2016
Dewey
Traitement du signal
Sujet
Descriptor; Fourier transform; Hexagonal grid; Geometric transformations; Support vector machine; Object recognition
Journal issue
Journal of Mathematical Imaging and Vision
Volume
57
Number
1
Publication date
01-2017
Article pages
117–133
Publisher
Kluwer Academic Publishers
DOI
http://dx.doi.org/10.1007/s10851-016-0669-1
URI
https://basepub.dauphine.fr/handle/123456789/17286
Collections
  • CEREMADE : Publications
Metadata
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Author
Bohi, Amine
199402 Laboratoire des Sciences de l'Information et des Systèmes [LSIS]
Prandi, Dario
60 CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Guis, Vincente
199402 Laboratoire des Sciences de l'Information et des Systèmes [LSIS]
Bouchara, Frédéric
199402 Laboratoire des Sciences de l'Information et des Systèmes [LSIS]
Gauthier, Jean-Paul
199402 Laboratoire des Sciences de l'Information et des Systèmes [LSIS]
Type
Article accepté pour publication ou publié
Abstract (EN)
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)=ZN⋊R2. 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.

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