Computationally efficient eigenspace decomposition of correlated images characterized by three parameters

  • K. Saitwal
  • A. A. MaciejewskiEmail author
  • R. G. Roberts
Theoretical Advances


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 



This work was supported in part by the National Imagery and Mapping Agency under contract no. NMA201-00-1-1003, through collaborative participation in the Robotics Consortium sponsored by the US Army Research Laboratory under the Collaborative Technology Alliance Program, Cooperative Agreement DAAD19-01-2-0012, and the Missile Defense Agency under the contract no. HQ0006-05-C-0035. Approved for Public Release 07-MDA-2783 (26 SEPT 07). The US Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation thereon. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the US Government. A preliminary version of portions of this work was presented at the IEEE Southwest Symposium on Image Analysis and Interpretation held at Denver, CO, USA, March 26–28, 2006.


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Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  • K. Saitwal
    • 1
  • A. A. Maciejewski
    • 2
    Email author
  • R. G. Roberts
    • 3
  1. 1.Behavioral Recognition Systems, Inc.HoustonUSA
  2. 2.Department of Electrical and Computer EngineeringColorado State UniversityFort CollinsUSA
  3. 3.Department of Electrical and Computer EngineeringFlorida A & M—Florida State UniversityTallahasseeUSA

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