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Measuring empirical discrepancy in image segmentation results

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3 Author(s)
Correa-Tome, F.E. ; DICIS, Univ. de Guanajuato, Salamanca, Mexico ; Sanchez-Yanez, R.E. ; Ayala-Ramirez, V.

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.

Published in:

Computer Vision, IET  (Volume:6 ,  Issue: 3 )