Probabilistic model for quick detection of dissimilar binary images

Adnan A. Y. Mustafa

doi:10.1117/1.JEI.24.5.053024

12 October 2015 Probabilistic model for quick detection of dissimilar binary images

Adnan A. Y. Mustafa

Author Affiliations +

Journal of Electronic Imaging, Vol. 24, Issue 5, 053024 (October 2015). https://doi.org/10.1117/1.JEI.24.5.053024

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.

Citation Download Citation

Adnan A. Y. Mustafa "Probabilistic model for quick detection of dissimilar binary images," Journal of Electronic Imaging 24(5), 053024 (12 October 2015). https://doi.org/10.1117/1.JEI.24.5.053024

Published: 12 October 2015

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