Using the low-resolution properties of correlated images to improve the computational efficiency of eigenspace decomposition
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 t...
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| Published in: | IEEE transactions on image processing Vol. 15; no. 8; pp. 2376 - 2387 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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New York, NY
IEEE
01-08-2006
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Draper, B.A. Roberts, R.G. Maciejewski, A.A. Saitwal, K. |
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| Keywords | 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) |
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| SubjectTerms | 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 |
| Title | Using the low-resolution properties of correlated images to improve the computational efficiency of eigenspace decomposition |
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