A Markov random field model-based approach to image interpretation
Modestino, J.W.
Zhang, J.
Dept. of Electr. Comput. & Syst. Eng., Rensselaer Polytech. Inst., Troy, New York, NY;
Abstract
A Markov random field (MRF) model-based approach to automated
image interpretation is described and demonstrated as a region-based
scheme. In this approach, an image is first segmented into a collection
of disjoint regions which form the nodes of an adjacency graph. Image
interpretation is then achieved through assigning object labels, or
interpretations, to the segmented regions, or nodes, using domain
knowledge, extracted feature measurements, and spatial relationships
between the various regions. The interpretation labels are modeled as a
MRF on the corresponding adjacency graph, and the image interpretation
problem are formulated as a maximum a posteriori estimation rule.
Simulated annealing is used to find the best realization, or optimal
interpretation. Through the MRF model, this approach also provides a
systematic method for organizing and representing domain knowledge
through the clique functions of the probability density function
underlying MRF. Results of image interpretation experiments performed on
synthetic and real-world images using this approach are described
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