From researching on the universal principle of feature fusion of image, a new algorithm was proposed which based on the 2 dimension principal component analyses (for short 2dpca).
通过对图像特征融合的一般规律的研究，提出了一种基于二维主成分分析(简称2dpca)的图像特征融合算法。
This dissertation mainly studied the image feature fusion based on PCA (principal component analyses) and its application of Small-weak target matching.
本文对基于主成分分析的特征级图像融合及其在弱小目标匹配识别上的应用做了一定的研究和探索。
Here, we use the singular value decomposition and principal component analysis for facial feature extraction, using the average distance category as discrimination on the basis of authentication.
其中人脸特征提取采用了奇异值分解和主分量分析法，身份验证则采用了以类内平均距离为判据的算法。
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