Open Access

Comparative Study of Contour Detection Evaluation Criteria Based on Dissimilarity Measures

  • Sébastien Chabrier1,
  • Hélène Laurent2Email author,
  • Christophe Rosenberger3 and
  • Bruno Emile2
EURASIP Journal on Image and Video Processing20082008:693053

DOI: 10.1155/2008/693053

Received: 18 July 2007

Accepted: 7 January 2008

Published: 28 February 2008


We present in this article a comparative study of well-known supervised evaluation criteria that enable the quantification of the quality of contour detection algorithms. The tested criteria are often used or combined in the literature to create new ones. Though these criteria are classical ones, none comparison has been made, on a large amount of data, to understand their relative behaviors. The objective of this article is to overcome this lack using large test databases both in a synthetic and a real context allowing a comparison in various situations and application fields and consequently to start a general comparison which could be extended by any person interested in this topic. After a review of the most common criteria used for the quantification of the quality of contour detection algorithms, their respective performances are presented using synthetic segmentation results in order to show their performance relevance face to undersegmentation, oversegmentation, or situations combining these two perturbations. These criteria are then tested on natural images in order to process the diversity of the possible encountered situations. The used databases and the following study can constitute the ground works for any researcher who wants to confront a new criterion face to well-known ones.

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Authors’ Affiliations

Laboratoire Terre-Océan, Université de la Polynésie Française
Institut PRISME, ENSI de Bourges, Université d'Orléans
Laboratoire GREYC, ENSICAEN, Université de Caen, CNRS, 6 boulevard du Maréchal Juin


© Sébastien Chabrier et al. 2008

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.