摘要
通常采用图像特征智能识别以提高对图像高频成分的识别度。基于大数据集域自适应快速算法构建图像特征智能识别模型。在图像特征智能识别模型构建过程中,对于图像特征智能识别过程中容易产生伪图像特征识别、细节性模糊与图像特征智能识别的不间断性问题,使用大数据集域自适应快速算法可以提高图像特征智能识别的效率。经过实验表明,基于大数据集域自适应快速算法的识别特征模型能够有效提升微小图像特征平均识别率,且鲁棒性较好。
The ultimate goal of intelligent recognition using image features is to increase the high-frequency components in intelligent recognition of image features(IRIF).In this paper,an intelligent recognition model of image feature based on large data domain adaptive fast algorithm(LDDAFA)is proposed.In the model structure,a large data domain adaptive fast algorithm is introduced in the construction of image feature intelligent recognition model,and the feature recognition and detail can be easily produced in the process of intelligent image recognition.In order to solve the problem of fuzzy and intelligent recognition of image features,the adaptive algorithm of large datasets is applied to intelligent recognition and detection of image features.The experiment shows that the recognition feature model building method based on the large data domain adaptive fast algorithm can accurately identify the signal to the SNR of different sags and ensure that the recognition accuracy can be improved effectively under the condition of low signal to noise ratio.
作者
王鹏宇
曾路
吴漾
Wang Pengyu;Zeng Lu;Wu Yang(Information Center,Guizhou Power Grid Corporation Ltd.,Guizhou 550002,China)
出处
《国外电子测量技术》
2019年第4期7-11,共5页
Foreign Electronic Measurement Technology
关键词
图像特征智能识别模型
振铃效应
大数据集域
自适应快速算法
intelligent recognition model of image features
ringing effect
large data set domain
adaptive fast algorithm
