Abstract
We propose a general Bayesian model for image segmentation with spatial coherence through a Markov Random Field prior. We also study variants of the model and their relationship. In this work we use the Matusita Distance, although our formulation admits other metric-divergences. Our main contributions in this work are the following. We propose a general MRF-based model for image segmentation. We study a model based on the Matusita Distance, whose solution is found directly in the discrete space with the advantage of working in a continuous space. We show experimentally that this model is competitive with other models of the state of the art. We propose a novel way to deal with non-linearities (irrational) related with the Matusita Distance. Finally, we propose an optimization method that allows us to obtain a hard image segmentation almost in real time and also prove its convergence.
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Dalmau, O., Rivera, M. (2009). A General Bayesian Markov Random Field Model for Probabilistic Image Segmentation. In: Wiederhold, P., Barneva, R.P. (eds) Combinatorial Image Analysis. IWCIA 2009. Lecture Notes in Computer Science, vol 5852. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10210-3_12
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DOI: https://doi.org/10.1007/978-3-642-10210-3_12
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