A multivariate distribution for sub-images

作者：Maybank S J^*

来源：Proceedings of the Royal Society A-Mathematical Physical and Engineering Sciences, 2009, 465(2103): 983-1001.

DOI：10.1098/rspa.2008.0212

• compressive sensing
• discrete cosine transform
• image statistics
• principal components analysis
• random projection
• stereo matching

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 Ornstein 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.

• 出版日期2009-3-8