A Unique Edge Detection Strategy Employing Neutrosophic r-cut with Enhanced User Interference
Subject Areas : 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.
Keywords: Neutrosophic Set, r-cut, Image Processing, Edge Detection.,
Abstract :
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.
[1] Smarandache F, A unifying field in logics: Neutrosophic logic. Neutrosophy, neutrosophic set, neutrosophic probability and statistics. 4th ed. Rehoboth: American Research Press; 1998. https://arxiv.org/pdf/math/0101228
[2] Guo Y, Cheng HD. New neutrosophic approach to image segmentation. Pattern Recognition. 2009; 42(5): 587-595. DOI: https://doi.org/10.1016/j.patcog.2008.10.002
[3] Sert E, Avci D. A new edge detection approach via neutrosophy based on maximum norm entropy. Expert Systems with Applications. 2019; 115: 499-511. DOI: http://dx.doi.org/10.1016/j.eswa.2018.08.019
[4] Das R, Datta M, Bal P, Nath H. Application of cylindrical (α, β)-cut of normalized pentapartitioned neutrosophic set in decision making using MATLAB. Mathematica Montisnigri. 2025; 62: (Accepted).
[5] Maini R, Aggarwal H. Study and comparison of various image edge detection techniques. International Journal of Image Processing (IJIP). 2009; 3(1): 1-12.
[6] Gao W, Zhang X, Yang L, Liu H. An improved sobel edge detection. 3rd International Conference on Computer Science and Information Technology. Chengdu: 2010. p. 67-71. DOI: https://doi.org/10.1109/ICCSIT.2010.5563693
[7] Shrivakshan GT, Chandrasekar C. A comparison of various edge detection techniques used in image processing. International Journal of Computer Science Issues. 2012; 9(5): 269-276.
[8] Chen H, Xu D, Yang Q, Liu S, Liu J, Luo H. Optical edge detection of object under ambient illumination by incorporating phase modulation with differential metasurface. Optics & Laser Technology. 2025; 180: 111416. DOI: https://doi.org/10.1016/j.optlastec.2024.111416
[9] Zadeh LA. Fuzzy sets. Information and Control. 1965; 8(3): 338-353. DOI: https://doi.org/10.1016/S0019-9958(65)90241-X
[10] Atanassov KT. Intuitionistic fuzzy sets. Fuzzy Sets and Systems. 1986; 20(1): 87-96. DOI: https://doi.org/10.1016/S0165-0114(86)80034-3
[11] Salama AA, Eisa M, Abdelmoghny MM. Neutrosophic relations database. International Journal of Information Science and Intelligent System. 2014; 3 (2): 33-46. DOI: http://dx.doi.org/10.5281/zenodo.23152