Akademik Veri Yönetim Sistemi
Measurement: Journal of the International Measurement Confederation, cilt.271, 2026 (SCI-Expanded, Scopus)
As a generalization of fuzzy sets, spherical fuzzy sets are an effective tool for modeling uncertain and complex structures. The edge detection process in image processing involves significant challenges due to noise, lighting variations, low-contrast regions, and uncertain boundaries. In particular, classical edge detection methods may exhibit certain limitations in modeling such uncertainties and, in some cases, may lead to weakening of edge information or incorrect edge detections. This situation highlights the need for new approaches that can handle uncertainty in a more flexible and comprehensive manner. This study presents a new approach to overcome these limitations by representing color images as spherical fuzzy sets in detail. A novel and effective edge detection method for color images has been developed using fuzzy similarity measures on spherical fuzzy sets. Furthermore, the theoretical framework of fuzzy similarity measures has been extended by proposing a very general function-based method for deriving similarity measures from distance measures, and vice versa. The proposed edge detection method has been tested on various color images, and the results obtained have been compared with classical edge detection methods. Experimental findings show that the developed approach enables edges to be detected more clearly and requiring less execution time.