To refer to this page use:
|Abstract:||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 essentially band limited 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. (C) 2013 Optical Society of America|
|Electronic Publication Date:||26-Mar-2013|
|Citation:||Zhao, Zhizhen, Singer, Amit. (2013). Fourier-Bessel rotational invariant eigenimages. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION, 30 (871 - 877. doi:10.1364/JOSAA.30.000871|
|Pages:||871 - 877|
|Type of Material:||Journal Article|
|Journal/Proceeding Title:||JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION|
Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.