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Edge detection method for color images based on $F^1$-transform and hierarchical fuzzy inference system | ||
| Computational Methods for Differential Equations | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 23 شهریور 1404 اصل مقاله (4.1 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22034/cmde.2025.66702.3288 | ||
| نویسندگان | ||
| Robab Alikhani* ؛ Maryam Ganjeh Alamdari | ||
| Department of Mathematics, University of Tabriz, Tabriz, Iran. | ||
| چکیده | ||
| This study presents a novel RGB image edge detection algorithm using a first-degree polynomial fuzzy transform ($F^1$-transform) and a hierarchical fuzzy inference system (HFIS). The $F^1$-transform generates adaptable convolution kernels that enable adjustable image smoothing and create directional derivatives in four orientations, producing an optimized gradient matrix. These derivatives help connect and refine edges into smooth, continuous contours. For computational efficiency, we implement a multi-layer HFIS structure with two-input single-output fuzzy systems at each layer. This hierarchy progressively refines fuzzified gradient inputs, enhancing edge extraction while reducing noise. The method shows improved performance with additional layers due to flexible rules and diverse membership functions. Comparisons with traditional (e.g., Canny) and modern techniques demonstrate its effectiveness. | ||
| کلیدواژه‌ها | ||
| $F^{1}$-transform؛ Hierarchical fuzzy inference system؛ Multi-input single-output fuzzy inference system؛ Optimized gradient matrix | ||
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آمار تعداد مشاهده مقاله: 2 |
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