Using the low-resolution properties of correlated images to improve the computational efficiency of eigenspace decomposition

• Published in: IEEE transactions on image processing Vol. 15; no. 8; pp. 2376 - 2387
• Main Authors: Saitwal, K., Maciejewski, A.A., Roberts, R.G., Draper, B.A.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-08-2006; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Application software; Applied sciences; Artificial Intelligence; Coding, codes; Collaborative work; Computation; Computational complexity; Computational efficiency; Computer science; control theory; systems; Computer vision; Correlation; data compression; Dealing; Detection, estimation, filtering, equalization, prediction; eigenspace; Exact sciences and technology; Government; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image processing; Image resolution; image sampling; image sequences; Information Storage and Retrieval - methods; Information, signal and communications theory; Mathematical analysis; Numerical Analysis, Computer-Assisted; Object recognition; Pattern Recognition, Automated - methods; Pattern recognition. Digital image processing. Computational geometry; Pixel; Signal and communications theory; Signal processing; Signal Processing, Computer-Assisted; Signal, noise; Singular value decomposition; singular value decomposition (SVD); Statistics as Topic; Subtraction Technique; Telecommunications and information theory; video coding; Video Recording - methods; Video coding; Performance evaluation; Computer vision; High resolution; Image resolution; Data compression; Video signal; Artificial vision; Image sampling; Low resolution; Algorithm; Computational complexity; eigenspace; Video signal processing; Image quality; Algorithm performance; correlation; Image sequence; A priori estimation; image sequences; Singular value decomposition; singular value decomposition (SVD)
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2006.875231

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 well on arbitrary video sequences.