12 October 2015 Probabilistic model for quick detection of dissimilar binary images
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J. of Electronic Imaging, 24(5), 053024 (2015). doi:10.1117/1.JEI.24.5.053024
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
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

Binary data

Image processing


Image segmentation

Associative arrays


Distance measurement


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