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Probabilistic model for quick detection of dissimilar binary images

[+] Author Affiliations
Adnan A. Y. Mustafa

Kuwait University, Department of Mechanical Engineering, P.O. Box 5969, Safat 13060, Kuwait

J. Electron. Imaging. 24(5), 053024 (Oct 12, 2015). doi:10.1117/1.JEI.24.5.053024
History: Received December 18, 2014; Accepted July 29, 2015
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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.

© 2015 SPIE and IS&T

Topics

Databases ; Displays ; Video

Citation

Adnan A. Y. Mustafa
"Probabilistic model for quick detection of dissimilar binary images", J. Electron. Imaging. 24(5), 053024 (Oct 12, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.5.053024


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