Maybank, Stephen J.
(2008)
A multivariate distribution for sub-images.
*Proceedings of the Royal Society of London, Series A* 465
(2103),
pp. 983-1001.
ISSN 0080-4630.

## Abstract

A new method for obtaining multivariate distributions for sub-images of natural images is described. The information in each sub-image is summarized by a measurement vector in a measurement space. The dimension of the measurement space is reduced by applying a random projection to the truncated output of the discrete cosine transforms of the sub-images. The measurement space is then reparametrized, such that a Gaussian distribution is a good model for the measurement vectors in the reparametrized space. An Ornsteinâ€“Uhlenbeck process, associated with the Gaussian distribution, is used to model the differences between measurement vectors obtained from matching sub-images. The probability of a false alarm and the probability of accepting a correct match are calculated. The accuracy of the resulting statistical model for matching sub-images is tested using images from the Middlebury stereo database with promising results. In particular, if the probability of accepting a correct match is relatively large, then there is good agreement between the calculated and the experimental probabilities of obtaining a unique match that is also a correct match.

Item Type: | Article |
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Keyword(s) / Subject(s): | compressive sensing, discrete cosine transform, image statistics, principal components analysis, random projection stereo matching |

School or Research Centre: | Birkbeck Schools and Research Centres > School of Business, Economics & Informatics > Computer Science and Information Systems |

Depositing User: | Administrator |

Date Deposited: | 02 Feb 2011 12:27 |

Last Modified: | 17 Apr 2013 12:18 |

URI: | http://eprints.bbk.ac.uk/id/eprint/1882 |

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