IEEE Trans Image Process - Computing steerable principal components of a large set of images and their rotations.

Tópicos

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Resumo

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.

Resumo Limpo

present effici algorithm comput princip compon analysi pca larg imag set consist imag imag set uniform rotat plane point block circul structur covari matrix util structur comput eigenvector also demonstr advantag algorithm similar one numer experi although use mani set illustr specif applic algorithm problem cryoelectron microscopi

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