Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fourier-Bessel rotational invariant eigenimages   We present an efficient and accurate algorithm for principal component
analysis (PCA) of a large set of two dimensional images, and, for each image,
the set of its uniform rotations in the plane and its reflection. The algorithm
starts by expanding each image, originally given on a Cartesian grid, in the
Fourier-Bessel basis for the disk. Because the images are bandlimited in the
Fourier domain, we use a sampling criterion to truncate the Fourier-Bessel
expansion such that the maximum amount of information is preserved without the
effect of aliasing. The constructed covariance matrix is invariant to rotation
and reflection and has a special block diagonal structure. PCA is efficiently
done for each block separately. This Fourier-Bessel based PCA detects more
meaningful eigenimages and has improved denoising capability compared to
traditional PCA for a finite number of noisy images.

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The categories of this paper are: cond-mat
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The categories of this paper are: supr-con
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The categories of this paper are: acc-phys
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The categories of this paper are: funct-an
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The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
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The categories of this paper are: eess
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The categories of this paper are: atom-ph
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The categories of this paper are: physics
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: gr-qc
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The categories of this paper are: adap-org
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The categories of this paper are: nucl-ex