计算机应用研究2013，Vol.30Issue(1)：285-287,3.DOI:10.3969/j.issn.1001-3695.2013.01.073
基于图像帧间信息和FS-KFDA的极光序列图像检测算法
Aurora time-series image detection based on sample inter-frame correlation and FS-KFDA
摘要
Abstract
Using artificial testing efficiently is technically difficult because of the large amounts of data which must be processed. So, according to the feature and correlation of aurora time-series image, this paper proposed an algorithm based on image segmentation to extract region of interest (ROI) of change. The analysis started with a feature extraction of the input sequence from the spatial domain. Then, it considered correlation between images in a close time sequence, proposed discrete wavelet transform(DWT) to analyze the correlation for the sake of their representative. It proposed K-means clustering to select training samples, and used feature-scaling kernel Fisher discriminant analysis (FS-KFDA) which was a modified kernel Fisher discriminant analysis to train and build classifiers to extract ROI base on the training samples. Experiments carried out on the real aurora image database from Chinese Arctic Yellowriver station point out the effectiveness of the proposed algorithm, which results in an increase of segmentation precision with respect to conventional algorithms.关键词
极光序列图像/核Fisher判别分析/感兴趣区域/相关性/特征提取/物理光学Key words
aurora time-series image/ kernel Fisher discriminant analysis/ ROI/ correlation/ feature extraction/ physical optics分类
信息技术与安全科学引用本文复制引用
卢山,焦李成,吴家骥,邓晓政..基于图像帧间信息和FS-KFDA的极光序列图像检测算法[J].计算机应用研究,2013,30(1):285-287,3.基金项目
国家自然科学基金资助项目(61072106,60970066,60972148,61077009,61075041,61001206,60803097,60970067,61003198) （61072106,60970066,60972148,61077009,61075041,61001206,60803097,60970067,61003198）
国家教育部博士点基金资助项目(200807010003) （200807010003）
国家部委科技资助项目(9140A07011810DZ0107,9140A07011810DZ0107,9140A07021010DZ0131) （9140A07011810DZ0107,9140A07011810DZ0107,9140A07021010DZ0131）
高等学校学科创新引智计划资助项目("111"计划)(B07048) （"111"计划）
