During the past few years several interesting applications of eigenspace representation of images have been proposed. These include face recognition, video coding, pose estimation, etc. However, the vision research community has largely overlooked parallel developments in signal processing and numerical linear algebra concerning efficient eigenspace updating algorithms. These new developments are significant for two reasons: adopting them makes some of the current vision algorithms more robust and efficient. More important is the fact that incremental updating of eigenspace representations opens up new and interesting research applications in vision such as active recognition and learning. The main objective of the paper is to put these in perspective and discuss a recently introduced updating scheme that has been shown to be numerically stable and optimal. We provide an example of one particular application to 3D object representation projections and give an error analysis of the algorithm. Preliminary experimental results are shown.
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