Designing eigenspace manifolds: with application to object identification and pose estimation

Authors:
Randy C. Hoover;Anthony A. Maciejewski;Rodney G. Roberts
Affiliations:
Dept. of Electrical and Computer Eng., Colorado State University, Fort Collins, CO;Dept. of Electrical and Computer Eng., Colorado State University, Fort Collins, CO;Dept. of Electrical and Computer Eng., Florida A & M - Florida State University, Tallahassee, FL
Venue:
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Year:
2009

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Abstract

Eigendecomposition has been used to classify threedimensional 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 for pose estimation. 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.