From Event: SPIE Optical Engineering + Applications, 2016
A Probabilistic Matching Model for Binary Images (PMMBI) is presented that predicts the probability of matching
binary images with any level of similarity. The model relates the number of mappings, the amount of similarity between
the images and the detection confidence. We show the advantage of using a probabilistic approach to matching in
similarity space as opposed to a linear search in size space. With PMMBI a complete model is available to predict the
quick detection of dissimilar binary images. Furthermore, the similarity between the images can be measured to a good
degree if the images are highly similar. PMMBI shows that only a few pixels need to be compared to detect dissimilarity
between images, as low as two pixels in some cases. PMMBI is image size invariant; images of any size can be matched
at the same quick speed. Near-duplicate images can also be detected without much difficulty. We present tests on real
images that show the prediction accuracy of the model.
Adnan A. Y. Mustafa, "Quick probabilistic binary image matching: changing the rules of the game," Proc. SPIE 9971, Applications of Digital Image Processing XXXIX, 997112 (Presented at SPIE Optical Engineering + Applications: August 31, 2016; Published: 27 September 2016); https://doi.org/10.1117/12.2237552.
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