Fast eigenspace decomposition of correlated images using their low-resolution properties

• Published in: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566) Vol. 3; pp. 2707 - 2712 vol.3
• Main Authors: Saitwal, K., Maciejewski, A.A., Roberts, R.G.
• Format: Conference Proceeding
• Language: English
• Published: Piscataway NJ IEEE 2004
• Subjects: Active appearance model; Algorithm design and analysis; Application software; Applied sciences; Artificial intelligence; Computational efficiency; Computer science; control theory; systems; Computer vision; Control theory. Systems; Exact sciences and technology; Face detection; Image analysis; Image resolution; Pattern recognition. Digital image processing. Computational geometry; Pixel; Robot vision systems; Computer vision; High resolution; Image resolution; Image processing; Image sequence; Artificial vision; Low resolution
• ISBN: 9780780384637; 0780384636
• DOI: 10.1109/IROS.2004.1389818

Eigendecomposition is a common technique that is performed on sets of correlated images in a number of computer vision and robotics applications. Unfortunately, the computation of an eigendecomposition can become prohibitively expensive when dealing with very high resolution images. While reducing the resolution of the images will reduce the computational expense, it is not known a priori how this will affect the quality of the resulting eigendecomposition. The work presented here provides an analysis of how different resolution reduction techniques affect the eigendecomposition. A computationally efficient algorithm for calculating the eigendecomposition based on this analysis is proposed. Examples show that this algorithm performs very well on arbitrary video sequences.