Fast Eigenspace Decomposition of Correlated Images
Fast Eigenspace Decomposition of Correlated Images
C-Y. Chang, A. A. Maciejewski and V. Balakrishnan
In Proc. Int. Conf. on Intelligent Robots and Systems (IROS), pp. 7-12,
Victoria, B.C., October 1998
Abstract:
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.