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A Method for Assessment of Segmentation Success Considering Uncertainty in the Edge Positions

  • Rubén UsamentiagaEmail author
  • Daniel F García
  • Carlos López
  • Diego González
Open Access
Research Article
Part of the following topical collections:
  1. Performance Evaluation in Image Processing

Abstract

A method for segmentation assessment is proposed. The technique is based on a comparison of the segmentation produced by an algorithm with an ideal segmentation. The procedure to obtain the ideal segmentation is described in detail. Uncertainty regarding the edge positions is accounted for in the discrepancy calculation of each edge using fuzzy reasoning. The uncertainty measurement consists of a generalization, using fuzzy membership functions, of the similarity metrics used by well-known assessment methods. Several alternatives for the fuzzy membership functions, based on statistical properties of the possible positions of each edge, are defined. The proposed uncertainty measurement can be easily applied to other well-known methods. Finally, the segmentation assessment method is used to determine the best segmentation algorithm for thermographic images, and also to tune the optimum parameters of each algorithm.

Keywords

Information Technology Optimum Parameter Quantum Information Assessment Method Uncertainty Measurement 

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Copyright information

© Rubén Usamentiaga et al. 2006

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Authors and Affiliations

  • Rubén Usamentiaga
    • 1
    Email author
  • Daniel F García
    • 1
  • Carlos López
    • 1
  • Diego González
    • 1
  1. 1.Department of Computer ScienceUniversity of OviedoGijónSpain

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