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Efficient Calculation of Primary Images from a Set of Images
May 1982 (vol. 4 no. 5)
pp. 511-515
Hiroyasu Murakami, Research and Development Section, Yanagicho Works, Toshiba Corporation, Kawasaki, Japan.
B. V. K. Vijaya Kumar, Department of Electrical Engineering, Carnegie-Mellon University, Pittsburgh, PA 15213.
A set of images is modeled as a stochastic process and Karhunen-Loeve expansion is applied to extract the feature images. Although the size of the correlation matrix for such a stochastic process is very large, we show the way to calculate the eigenvectors when the rank of the correlation matrix is not large. We also propose an iterative algorithm to calculate the eigenvectors which save computation time andc omputer storage requirements. This iterative algorithm gains its efficiency from the fact that only a significant set of eigenvectors are retained at any stage of iteration. Simulation results are also presented to verify these methods.
Citation:
Hiroyasu Murakami, B. V. K. Vijaya Kumar, "Efficient Calculation of Primary Images from a Set of Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 4, no. 5, pp. 511-515, May 1982, doi:10.1109/TPAMI.1982.4767295
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