Image Segmentation Methods for Non-destructive testing Applications
This work addresses image segmentation for non-destructive testing applications, but it appears incremental as it combines existing techniques (HMRFs and CS variants) without introducing a fundamentally new approach.
The paper tackled image segmentation for non-destructive testing by developing methods based on hidden Markov random fields and cuckoo search variants, resulting in evaluation of five CS variants using misclassification error to select optimal parameters for execution time and segmentation quality.
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.