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Improved textured images segmentation using an energy functional

  • Antoni Grau
  • Jordi Saludes
Poster Session A: Color & Texture, Enhancement, Image Analysis & Pattern Recognition, Segmentation
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1310)

Abstract

In this paper we present a new classification and image segmentation system based on the addition of a variational method to a classic clustering algorithm. This system constitutes an improvement respect traditional segmentation methods. Often due to the nature of the texture features obtained from an image, the segmentation results are not quite precise. If this happens, using the energy functional and its minimization can improve the segmentation. This functional takes into account the information in the feature space and the information in the 2D image domain. The extracted characteristics from the image are texture features that have been defined in order to obtain an admissible trade-off between their discriminant capacity and their effectiveness to be implemented in a vision board to operate at real time. We show some results to appreciate this improvement in the segmentation using the energy functional.

Keywords

Feature Space Image Segmentation Input Image Texture Feature Learning Phase 
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

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Antoni Grau
    • 1
  • Jordi Saludes
    • 2
  1. 1.Dept. of Automatic Control and Computer EngineeringPolytechnic University of Catalonia UPCBarcelonaCatalonia
  2. 2.Dept. of Applied Mathematics IIPolytechnic University of Catalonia UPCBarcelonaCatalonia

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