Fast eigenspace decomposition of correlated images Chang, C-Y. ; Maciejewski, Anthony A. ; Balakrishnan, Venkataramanan "This work was supported by the Sze Tsao Chang Memorial Engineering Fund and by the Office of Naval Research under contract no. N00014-97-1-0540." We present a computationally efficient algorithm for the eigenspace decomposition of correlated images. Our approach is motivated by the fact that for a planar rotation of a two-dimensional image, analytical expressions can be given for the eigendecomposition, based on the theory of circulant matrices. These analytical expressions turn out to be good first approximations of the eigendecomposition, even for three-dimensional objects rotated about a single axis. We use this observation to automatically determine the dimension of the subspace required to represent an image with a guaranteed user-specified accuracy, as well as to quickly compute a basis for the subspace. Examples show that the algorithm performs very well on a range of test images composed of three-dimensional objects rotated about a single axis. Colorado State University. Libraries 1998 text ; image application/pdf ECEaam00084.pdf FACFECEN100084ARTI eng c1998 IEEE
Fast eigenspace decomposition of correlated images
Chang, C-Y. ; Maciejewski, Anthony A. ; Balakrishnan, Venkataramanan
"This work was supported by the Sze Tsao Chang Memorial Engineering Fund and by the Office of Naval Research under contract no. N00014-97-1-0540."
We present a computationally efficient algorithm for the eigenspace decomposition of correlated images. Our approach is motivated by the fact that for a planar rotation of a two-dimensional image, analytical expressions can be given for the eigendecomposition, based on the theory of circulant matrices. These analytical expressions turn out to be good first approximations of the eigendecomposition, even for three-dimensional objects rotated about a single axis. We use this observation to automatically determine the dimension of the subspace required to represent an image with a guaranteed user-specified accuracy, as well as to quickly compute a basis for the subspace. Examples show that the algorithm performs very well on a range of test images composed of three-dimensional objects rotated about a single axis.
Colorado State University. Libraries
1998
text ; image
application/pdf
ECEaam00084.pdf
FACFECEN100084ARTI
eng
c1998 IEEE