Image Segmentation Methods for Non-destructive testing Applications

EL-Hachemi Guerrout; Ramdane Mahiou; Randa Boukabene; Assia Ouali

Image Segmentation Methods for Non-destructive testing Applications

Computer Vision and Pattern Recognition 2021-03-16 v1 Artificial Intelligence

Authors: EL-Hachemi Guerrout , Ramdane Mahiou , Randa Boukabene , Assia Ouali

Abstract

In this paper, we present new image segmentation methods based on hidden Markov random fields (HMRFs) and cuckoo search (CS) variants. HMRFs model the segmentation problem as a minimization of an energy function. CS algorithm is one of the recent powerful optimization techniques. Therefore, five variants of the CS algorithm are used to compute a solution. Through tests, we conduct a study to choose the CS variant with parameters that give good results (execution time and quality of segmentation). CS variants are evaluated and compared with non-destructive testing (NDT) images using a misclassification error (ME) criterion.

Keywords

image segmentation medical image analysis matrix factorization

Cite

@article{arxiv.2103.07754,
  title  = {Image Segmentation Methods for Non-destructive testing Applications},
  author = {EL-Hachemi Guerrout and Ramdane Mahiou and Randa Boukabene and Assia Ouali},
  journal= {arXiv preprint arXiv:2103.07754},
  year   = {2021}
}

Comments

10 pages, 3 figures, the article is just accepted in the conference JERI 2020 but the conference stopped because of Covid so the article non published

Image Segmentation Methods for Non-destructive testing Applications

Abstract

Keywords

Cite

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