Eigendecomposition of images correlated on S1, S2, and SO(3) using spectral theory
IEEE Transactions on Image Processing
Designing eigenspace manifolds: with application to object identification and pose estimation
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
An illustration of eigenspace decomposition for illumination invariant pose estimation
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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In this paper, we consider pose estimation of 3-D objects from an aerial perspective using eigendecomposition. We first outline a sampling method to acquire images of the object from an aerial view by sampling the upper unit hemisphere. Using this hemispherical sampling pattern, the computational burden of computing the eigendecomposition can be reduced by using the HemiSpherical Harmonic Transform (HSHT) to "condense" information due to the hemispherical correlation. We then propose a computationally efficient algorithm for approximating the eigendecomposition based on the HSHT analysis.