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• [P361] Designing Eigenspace Manifolds: With Application to Object Identification and Pose Estimation
  Author(s): Randy C. Hoover, Anthony A. Maciejewski, Rodney G. Roberts
  
  

  Abstract
  Eigendecomposition has been used to classify three-dimensional objects from two-dimensional images in a variety of computer vision and robotics applications. The biggest on-line computational expense associated with using eigendecomposition is the determination of the closest point on an image manifold embedded in a high dimensional space. The dimensionality and complexity of the space is a result of the $p$ principal eigenimages that are selected. Unfortunately, for some real-time applications, this search may be prohibitively expensive. This work presents, a method to reduce the on-line expense associated with using eigendecomposition. The approach is based on selecting a linear combination of the principal eigenimages to design an eigenspace manifold having a desirable geometric structure that reduces the cost associated with classification.

  
   

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