邊緣檢測是圖像預(yù)處理中最重要的內(nèi)容之一，本文使用遺傳算法對閾值優(yōu)化得到最佳閾值參數(shù)，對模糊邊緣檢測算法進(jìn)行改進(jìn)，根據(jù)此最佳閾值來定義一個(gè)新的簡單隸屬度函數(shù)，簡化了Pal.King算法中復(fù)雜的G和G-1運(yùn)算。不僅使復(fù)雜計(jì)算簡單化，還減少了迭代次數(shù)。仿真結(jié)果表明：該算法具有較強(qiáng)的檢測模糊邊緣能力,是一種實(shí)用、高效的邊緣提取算法，同時(shí)此方法很容易擴(kuò)展到多閾值圖像邊緣處理。
關(guān)鍵詞: 邊緣檢測；隸屬函數(shù)；模糊特征平面；模糊增強(qiáng)；閾值優(yōu)化
Fuzzy edge detection based on threshold optimization Luo yu-ling(1) (2) Tang xian-ying(1)
1(Institute of Computer and Communication Engineering, Changsha University Of Science and Technology, HuNan, 410076, China) 2 (Department of Computer Science, XiangNan University Of Science and Technology, HuNan. 423000, China) Abstract：Edge detection is one of the most important elements in Image processing. This Paper makes some improvement to the Pal. King fuzzy edge-detection algorithm. This is present a scheme for optimal thresholding using genetic algorithms. A new definition of a simple membership function according to the optimal thresholding. Simplified complex G and G-1 operations of Pal. King Algorithms. This improved algorithm not only simplifies complex calculation, but also brings out a new transformation of fuzzy enhancement, and reduces the count of iterations. Simulation results show that: the algorithm is a practical and efficient edge detection algorithm, and this method can easily extend to more threshold edge detection of image processing.
Keywords: edge detection；membership function；Fuzzy property domain；Fuzzy enhancement；Threshold optimization