计算机应用 ›› 2010, Vol. 30 ›› Issue (11): 2983-2985.
• 模式识别 • 上一篇 下一篇
李全文1,阮波2,徐可佳3,于勇3,肖劲飞4
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摘要： 在主成分分析(PCA)及核主成分分析(KPCA)进行特征提取基本原理的基础上,提出了一种改进的提取非线性的图像特征来重建图像方法,应用于嵌入式防伪水印图案缺陷的检测。该方法使得图像协方差矩阵维数大幅下降,且有效地保留了嵌入式防伪水印图案的信息,通过比较检测出图像的缺陷。实验结果表明,该方法对输入数据实现了有效的降维,缩短了计算时间,提高了检测效果和精确度。KPCA算法相比原有的PCA算法具有更高的性能指标,适用范围更广。
关键词: 核主成分分析, 水印图案, 缺陷检测
Abstract: Based on the fundamental principles of feature extraction of PCA and KPCA, an improved method to extract features of non-linear image so as to rebuild image was proposed for detecting embedded watermark image defects. This method decreased greatly the dimension of Core Matrix and kept the information of embedded watermark image effectively. Thus the defects of images could be found out through comparison. The experimental results show that this method enables input data to reduce dimension effectively, shortens computation time and improves detection effect and accuracy. KPCA has a higher performance index and wider range of application than PCA.
Key words: Kernel Principal Component Analysis (KPCA), watermark image, defect detecting
李全文 阮波 徐可佳 于勇 肖劲飞. 低对比度下水印图像缺陷检测[J]. 计算机应用, 2010, 30(11): 2983-2985.
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http://www.joca.cn/CN/Y2010/V30/I11/2983
