AN EIGENSPACE UPDATE ALGORITHM FOR IMAGE-ANALYSIS

During the past few years several interesting applications of eigenspa ce representation of images have been proposed. These include face rec ognition, video coding, and pose estimation. However, the vision resea rch community has largely overlooked parallel developments in signal p rocessing and numerical li;lear algebra concerning efficient eigenspac e updating algorithms. These new developments are significant for two reasons: Adopting them will make some of the current vision algorithms more robust and efficient, More important is the fact that incrementa l updating of eigenspace representations will open up new and interest ing research applications in vision such as active recognition and lea rning. The main objective of this paper is to put these in perspective and discuss a new updating scheme for low numerical rank matrices tha t can be shown to be numerically stable and fast. A comparison with a nonadaptive SVD scheme shows that our algorithm achieves similar accur acy levels for image reconstruction and recognition at a significantly lower computational cost. We also illustrate applications to adaptive view selection for 3D object representation from projections. (C) 199 7 Academic Press.