• Aucun résultat trouvé

Error measures for segmentation results:Evaluation on synthetic images

N/A
N/A
Protected

Academic year: 2021

Partager "Error measures for segmentation results:Evaluation on synthetic images"

Copied!
1
0
0

Texte intégral

(1)

IEEE International Conference on Electronics, Circuits, and Systems (ICECS) 2010

Error measures for segmentation results:

Evaluation on synthetic images

A. B. Goumeidane, M. Khamadja

Abstract : In this paper we present a new approach to assess image segmentation results. This approach consists of six measures that aim, together, to compute in the sense of segmentation evaluation, the discrepancy between a region extracted from a segmentation map and its corresponding one on the reference map. Those measures take into account many aspects of the miss-segmented pixels as connectivity, compactness, location and their influence on the segmentation results.

Keywords : image segmentation, Results evaluation, Supervised evaluation

Centre de Recherche en Technologies Industrielles - CRTI - www.crti.dz

Powered by TCPDF (www.tcpdf.org)

Références

Documents relatifs

basée sur l‟algorithme du FCM qui comporte les cinq étapes suivantes : la classification par l‟algorithme FCM, la fusion des classes, l‟étiquetage, la suppression

Abstract : In this paper, we propose new evaluation measures for scene segmentation results, which are based on computing the difference between a region extracted from a

In this paper, we propose new evaluation measures /or scene segmentation results, which are based on computing the difference between a region extracted from

This method consists of an association of measures that have the purpose to compute, in the sense of segmentation evaluation, the difference between two regions, one extracted from

This method consists of an association of measures that have the purpose to compute, in the sense of segmentation evaluation, the difference between two

Segmentation results are given in table 3 which dis- plays the MSD distance between ground truth curves and the curves obtained with our method for the M 1 speaker (28 sounds)..

Ill-posedness of model-based (or generative model) formulations of the segmentation problem have been extensively discussed in the energy minimization-based segmentation community,

This article presents a framework for the texture-based seg- mentation of historical digitized book content with little a priori knowledge in order to evaluate three approaches based