基于中智集的自适应阈值边缘检测算法
Adaptive Threshold Edge Detection Algorithm Based on Neutrosophic Set
胡朝辉 1邹钊斌 1莫帅2
作者信息
- 1. 湖南大学,机械与运载工程学院,长沙 410082
- 2. 广西大学,省部共建特色金属材料与组合结构全寿命安全国家重点实验室,南宁 530004
- 折叠
摘要
为提升边缘检测的速度、精度及抗噪性,提出了一种基于中智集的自适应阈值边缘检测算法,该算法通过边窗滤波算法代替传统滤波算法进行去噪;改进中智集获取算法,将图像分割为T、F、I 3个子集,缩短处理时间;提出自适应阈值提取算法,缩短阈值提取时间;最后融合分割信息,获取边缘特征.试验表明,该算法在处理不同噪声时效果优于最新研究的基于最大熵的中智集边缘检测算法(NMNE),在保证精度的同时显著提升了检测速度.
Abstract
To enhance the speed,accuracy,and noise resistance of edge detection,an adaptive threshold edge detection algorithm based on the Neutrosophic Set(NS)is proposed.This algorithm employs a side-window filtering approach to replace traditional filtering algorithms for noise reduction.The NS acquisition algorithm is improved by dividing the image into three subsets:True(T),False(F),and Indeterminate(I),thereby reducing processing time.An adaptive threshold extraction algorithm is introduced to shorten the threshold extraction process.Finally,the segmentation information is fused to obtain the edge features.Experimental results demonstrate that this algorithm outperforms the newly studied Neutrosophic Set and Maximum Norm Entropy(NMNE)edge detection algorithm when dealing with various types of noise.It significantly improves detection speed while ensuring accuracy.
关键词
图像处理/边缘检测/中智集/自适应阈值Key words
Image processing/Edge detection/Neutrosophic set/Adaptive threshold引用本文复制引用
出版年
2024
NETL
NSTL
万方数据