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Journal of Electronic Imaging

Probabilistic model for quick detection of dissimilar binary images
Author(s): Adnan A. Y. Mustafa
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Paper Abstract

We present a quick method to detect dissimilar binary images. The method is based on a “probabilistic matching model” for image matching. The matching model is used to predict the probability of occurrence of distinct-dissimilar image pairs (completely different images) when matching one image to another. Based on this model, distinct-dissimilar images can be detected by matching only a few points between two images with high confidence, namely 11 points for a 99.9% successful detection rate. For image pairs that are dissimilar but not distinct-dissimilar, more points need to be mapped. The number of points required to attain a certain successful detection rate or confidence depends on the amount of similarity between the compared images. As this similarity increases, more points are required. For example, images that differ by 1% can be detected by mapping fewer than 70 points on average. More importantly, the model is image size invariant; so, images of any sizes will produce high confidence levels with a limited number of matched points. As a result, this method does not suffer from the image size handicap that impedes current methods. We report on extensive tests conducted on real images of different sizes.

Paper Details

Date Published: 12 October 2015
PDF: 29 pages
J. Electron. Imaging. 24(5) 053024 doi: 10.1117/1.JEI.24.5.053024
Published in: Journal of Electronic Imaging Volume 24, Issue 5
Show Author Affiliations
Adnan A. Y. Mustafa, Kuwait Univ. (Kuwait)


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