Enhanced GMD Technique for Segmentation of Yarn Images based on Parametric Value and Intensity Gradient Analysis - Muthusamy, R. Sudha; Chitraa, V (International Journal of Computer Sciences and Engineering, 2025)
Because of the approach of computer based innovation image processing strategies have turned out to be progressively vital in a wide assortment of utilizations. Segmentation is an exemplary subject in the field of image processing. Many works are existing in the area of segmentation and these systems regularly must be joined with area of learning new innovations techniques to achieve the end goal to adequately tackle the segmentation issue. This paper discuss the issue of segmenting yarn image with fuzzy and coherence techniques. The point of segmentation in the considered application is to remove yarn core from the yarn. The technique is guided and compelled by Coherence Enhancing Diffusion (CED) and FCM (Fuzzy C-Means) channel and furthermore the main problem of the yarn image segmentation is considered. For the process of segmentation Gaussian mixture model in enhanced with coherence and fuzzy to acquire a division limit esteem. The Results of segmentation by the CED, FCM and proposed strategy GMD are compared. The correlation demonstrates that the proposed method gives best outcomes. The yarn core and the hairiness segmentation from the proposed algorithm are adequate for assurance of yarn properties performed in the accompanying strides of the estimations of yarn hairiness measurements