A Probabilistic Model for Random Binary Image Mapping

Abstract

Many probabilistic models have been developed for numerous problems in robot and computer vision such as for image segmentation, road extraction, and object tracking. In this paper we present a probabilistic model for the random pixel mapping of binary images. The model predicts the probability of detecting dissimilarity between dissimilar binary images as a function of the number of random mappings and the amount of similarity. The model shows that detecting dissimilarity can be accomplished quickly by random pixel mapping, without the need to process the entire image. Test results on real images are presented that show the accuracy of the model.