Computing Steerable Principal Components of a Large Set of Images and Their Rotations

被引:12
|
作者
Ponce, Colin [1 ]
Singer, Amit [2 ,3 ]
机构
[1] Cornell Univ, Dept Comp Sci, Ithaca, NY 14850 USA
[2] Princeton Univ, Dept Math, Princeton, NJ 08544 USA
[3] Princeton Univ, PACM, Princeton, NJ 08544 USA
关键词
EDICS Category: TEC-PRC image and video processing techniques; KARHUNEN-LOEVE EXPANSION; DISCRETE COSINE TRANSFORM; UNIFORMLY ROTATED IMAGES; OPTIMAL APPROXIMATION; CLASSIFICATION;
D O I
10.1109/TIP.2011.2147323
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We present here an efficient algorithm to compute the Principal Component Analysis (PCA) of a large image set consisting of images and, for each image, the set of its uniform rotations in the plane. We do this by pointing out the block circulant structure of the covariance matrix and utilizing that structure to compute its eigenvectors. We also demonstrate the advantages of this algorithm over similar ones with numerical experiments. Although it is useful in many settings, we illustrate the specific application of the algorithm to the problem of cryo-electron microscopy.
引用
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页码:3051 / 3062
页数:12
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