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Image segmentation by means of fuzzy entropy measure

  • C. Di Ruberto
  • M. Nappi
  • S. Vitulano
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

The paper describes an algorithm for image segmentation using fuzzy entropy measure. The relation between the fuzzy entropy of an image domain and the fuzzy entropy of its subdomains is explored as a uniformity predicate. With the aim of implementing the model, we have introduced a well known technique of Problem Solving. The most important roles of our model are played by the Evaluation Function (EF) and the Control Strategy. So the EF is related to the ratio between the fuzzy entropy of one region or zone of the picture and the fuzzy entropy of the entire picture. The Control Strategy determines the optimal path in the search tree (quadtree) so that the nodes of the optimal path have minimal fuzzy entropy. The paper shows some comparisons between the proposed algorithm and classical edge detection techniques.

Keywords

Image Segmentation Edge Detection Search Tree Child Node Local Operator 
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

  • C. Di Ruberto
    • 1
  • M. Nappi
    • 1
  • S. Vitulano
    • 1
  1. 1.Istituto di Medicina InternaPoliclinico UniversitarioCagliariItaly

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