Computationally efficient eigenspace decomposition of correlated images characterized by three parameters
Eigendecomposition is a common technique that is performed on sets of correlated images in a number of pattern recognition applications including object detection and pose estimation. However, many fast eigendecomposition algorithms rely on correlated images that are, at least implicitly, characterized by only one parameter, frequently time, for their computational efficacy. In some applications, e.g., three-dimensional pose estimation, images are correlated along multiple parameters and no natural one-dimensional ordering exists. In this work, a fast eigendecomposition algorithm that exploits the temporal correlation within image data sets characterized by one parameter is extended to improve the computational efficiency of computing the eigendecomposition for image data sets characterized by three parameters. The algorithm is implemented and evaluated using three-dimensional pose estimation as an example application. Its accuracy and computational efficiency are compared to that of the original algorithm applied to one-dimensional pose estimation.
eigenspace singular value decomposition computational complexity image sequences three-dimensional correlations computer vision