Eigendecomposition of Correlated Images Characterized by Three Parameters

Most eigendecomposition algorithms operate on correlated images that are characterized by only one parameter. Hence they lack the required specifications of fully general 3D image data sets, in which the images need to be characterized by three parameters. In this paper, an extension of one of the fastest known eigendecomposition algorithms is successfully implemented to improve the computational efficiency of computing the eigendecomposition of such 3D image sets. This algorithm can be used in pattern recognition applications such as fully general 3D pose estimation of objects