Neural Processing Letters

, Volume 1, Issue 1, pp 25–29 | Cite as

Texture segmentation using pyramidal Gabor functions and self-organising feature maps

  • A. Guérin-Dugué
  • P. M. Palagi


This paper presents texture segmentation realised with image treatment methods and an artificial neural network model. Gabor oriented filters are used to extract frequential texture features and Self-Organising Feature Maps are used to group and interpolate these features. In order to decrease the number of filters, we use a pyramidal multiresolution method of image representation. We intend to build an architecture inspired by the early stages of the visual cortex, while making local frequential analysis of the images, which must be able to segment different textured images.


Neural Network Artificial Neural Network Network Model Nonlinear Dynamics Visual Cortex 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Kluwer Academic Publishers 1994

Authors and Affiliations

  • A. Guérin-Dugué
    • 1
  • P. M. Palagi
    • 1
  1. 1.Laboratoire de Traitement d'Images et Reconnaissance de FormesInstitut National Polytechnique de GrenobleGrenoble CedexFrance

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