Image Threshold Selection Exploiting Empirical Mode Decomposition

Abstract : Thresholding process is a fundamental image processing method. Typical thresholding methods are based on partitioning pixels in an image into two clusters. A new thresholding method is presented, in this paper. The main contribution of the proposed approach is the detection of an optimal image threshold exploiting the empirical mode decomposition (EMD) algorithm. The EMD algorithm can decompose any nonlinear and non-stationary data into a number of intrinsic mode functions (IMFs). When the image is decomposed by empirical mode decomposition (EMD), the intermediate IMFs of the image histogram have very good characteristics on image thresholding. The experimental results are provided to show the effectiveness of the proposed threshold selection method.
Type de document :
Communication dans un congrès
Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-381 (Part I), pp.395-403, 2012, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-33409-2_41〉
Liste complète des métadonnées

Littérature citée [20 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01521420
Contributeur : Hal Ifip <>
Soumis le : jeudi 11 mai 2017 - 17:10:39
Dernière modification le : vendredi 1 décembre 2017 - 01:16:31
Document(s) archivé(s) le : samedi 12 août 2017 - 14:01:37

Fichier

978-3-642-33409-2_41_Chapter.p...
Fichiers produits par l'(les) auteur(s)

Licence


Distributed under a Creative Commons Paternité 4.0 International License

Identifiants

Citation

Stelios Krinidis, Michail Krinidis. Image Threshold Selection Exploiting Empirical Mode Decomposition. Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-381 (Part I), pp.395-403, 2012, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-33409-2_41〉. 〈hal-01521420〉

Partager

Métriques

Consultations de la notice

119

Téléchargements de fichiers

29