Adnan A. Y. Mustafa, "A Complete Probabilistic Model for the Quick Detection of Dissimilar Binary Images by Random Intensity Mapping," WSEAS Transactions on Signal Processing, vol. 13, pp. 208-214, 2017, DOI:
Adnan A. Y. Mustafa. A Complete Probabilistic Model for the Quick Detection of Dissimilar Binary Images by Random Intensity Mapping.
WSEAS Transactions on Signal Processing. 2017;13:208-214.
WSEAS Transactions on Signal Processing
Print ISSN: 1790-5052, E-ISSN: 2224-3488
Volume 13, 2017
A Complete Probabilistic Model for the Quick Detection of Dissimilar Binary Images by Random Intensity Mapping
Author:
Abstract: In this paper we present the Probabilistic Matching Model for Binary Images (PMMBI), a model for the quick detection of dissimilar binary images based on random point mappings. The model predicts the probability of detecting dissimilarity between any pair of binary images based on the amount of similarity and number of random pixel mappings between them. Based on the model, we show that by performing a limited number of random pixel mappings between binary images, dissimilarity detection can be performed quickly. Furthermore, the model is image size invariant; the size of the image has absolutely no effect on the dissimilarity detection quickness. We give examples with real images to show the accuracy of the model.
Pages: 208-214 WSEAS Transactions on Signal Processing, ISSN / E-ISSN: 1790-5052 / 2224-3488, Volume 13, 2017, Art. #23 DOI: *As the DOI is a unique identifier, it is already available in the pdf version.
**The DOI link will be activated in the first midst of January 2026.
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