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Proceedings Paper

Information in the joint aggregate pixel distribution of two images
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Paper Abstract

Pixel value distributions of most real images have structure that cannot be modeled by simple and commonly used probability distributions, such as Gaussian or log-normal distributions. Estimation of pixel value distribution in the joint measurement space ( JMS ) of two real images reveals the joint density structure and allows its interpretation by means of statistical dependence measures. A dependence measure is a general way to express similarity or divergence between images. Candidate dependence measures include adaptations of information measures such as Shannon Entropy and Fisher Information. The dependence measure built from Fisher Information is tested and demonstrated by experiments in Independent Components Analysis ( ICA ) and co-registration of synthetic and real Landsat TM images, including successful co-registration of images from different spectral bands with zero linear correlation.

Paper Details

Date Published: 25 May 2005
PDF: 12 pages
Proc. SPIE 5817, Visual Information Processing XIV, (25 May 2005); doi: 10.1117/12.602662
Show Author Affiliations
Wit T. Wisniewski, The Univ. of Arizona (United States)
Robert A. Schowengerdt, The Univ. of Arizona (United States)

Published in SPIE Proceedings Vol. 5817:
Visual Information Processing XIV
Zia-ur Rahman; Robert A. Schowengerdt; Stephen E. Reichenbach, Editor(s)

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