A Unique Edge Detection Strategy Employing Neutrosophic r-cut with Enhanced User Interference
محورهای موضوعی : Transactions on Fuzzy Sets and Systems
Himangshu Nath
1
,
Prasenjit Bal
2
,
Mithun Datta
3
1 - Department of Mathematics, ICFAI University Tripura, Agartala, India.
2 - Department of Mathematics, ICFAI University Tripura, Agartala, India.
3 - Department of Mathematics, ICFAI University Tripura, Agartala, India.
کلید واژه: Neutrosophic Set, r-cut, Image Processing, Edge Detection.,
چکیده مقاله :
Neutrosophic set (NS) theory provides a foundation for investigating the nature and implications of neutral elements within various conceptual frameworks. While NS offers a general framework, its practical application requires specific adaptations to suit particular domains. This research focuses on applying NS to the field of image segmentation. By representing images in the NS domain using truth (T), indeterminacy (I), and falsity (F) membership functions, we introduce a novel entropy measure to quantify image uncertainty. An r-cutbased segmentation method is developed to partition images effectively. Experimental results validate the proposed approach’s ability to segment images across different values of r, demonstrating its robustness in handling both clean and noisy image conditions.
Neutrosophic set (NS) theory provides a foundation for investigating the nature and implications of neutral elements within various conceptual frameworks. While NS offers a general framework, its practical application requires specific adaptations to suit particular domains. This research focuses on applying NS to the field of image segmentation. By representing images in the NS domain using truth (T), indeterminacy (I), and falsity (F) membership functions, we introduce a novel entropy measure to quantify image uncertainty. An r-cutbased segmentation method is developed to partition images effectively. Experimental results validate the proposed approach’s ability to segment images across different values of r, demonstrating its robustness in handling both clean and noisy image conditions.
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