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

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Abstract

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.

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Authors and Affiliations

  1. Laboratoire de Traitement d'Images et Reconnaissance de Formes, Institut National Polytechnique de Grenoble, 46 Av. Félix Viallet, F-38031, Grenoble Cedex, France

    A. Guérin-Dugué & P. M. Palagi

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  1. A. Guérin-Dugué
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  2. P. M. Palagi
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Guérin-Dugué, A., Palagi, P.M. Texture segmentation using pyramidal Gabor functions and self-organising feature maps. Neural Process Lett 1, 25–29 (1994). https://doi.org/10.1007/BF02312398

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