Measuring empirical discrepancy in image segmentation results

Author(s): F.E. Correa-Tome ; R.E. Sanchez-Yanez ; V. Ayala-Ramirez
DOI: 10.1049/iet-cvi.2010.0179

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Author(s): F.E. Correa-Tome ¹ ; R.E. Sanchez-Yanez ¹ ; V. Ayala-Ramirez ¹
- Affiliations: 1: Universidad de Guanajuato DICIS, Salamanca Gto., Mexico
Source: Volume 6, Issue 3, May 2012, p. 224 – 230
DOI: 10.1049/iet-cvi.2010.0179 , Print ISSN 1751-9632, Online ISSN 1751-9640

A methodology for comparison of boundary and segmentation images based on Precision–Recall graphs is presented in this study. The proposed methodology compares the location of edge pixels between an image under test and an ideal reference, in order to obtain a precise normalised similarity measure. This approach also deals with the case when multiple references are available using a merging procedure. Small displacement errors in edge pixel location are handled using a tolerance radius, which introduces the problem of multiple matching between test and reference edge pixels. This problem is addressed as a bipartite graph, solved by using the Hopcroft–Karp algorithm to obtain the maximum number of unique matchings. Experiments have been carried out in order to determine the performance of this evaluation approach.

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Measuring empirical discrepancy in image segmentation results

Measuring empirical discrepancy in image segmentation results

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