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

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 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.