Abstract
We propose a multi-layer binary Markov random field (MRF) model that assigns high probability to object configurations in the image domain consisting of an unknown number of possibly touching or overlapping near-circular objects of approximately a given size. Each layer has an associated binary field that specifies a region corresponding to objects. Overlapping objects are represented by regions in different layers. Within each layer, long-range interactions favor connected components of approximately circular shape, while regions in different layers that overlap are penalized. Used as a prior coupled with a suitable data likelihood, the model can be used for object extraction from images, e.g. cells in biological images or densely-packed tree crowns in remote sensing images. We present a theoretical and experimental analysis of the model, and demonstrate its performance on various synthetic and biomedical images.
This research was partially supported by the grant CNK80370 of the National Office for Research and Technology (NKTH) & Hungarian Scientific Research Fund (OTKA); by the European Union and co-financed by the European Regional Development Fund within the project TAMOP-4.2.1/B-09/1/KONV-2010-0005.
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Nemeth, J., Kato, Z., Jermyn, I. (2011). A Multi-Layer ‘Gas of Circles’ Markov Random Field Model for the Extraction of Overlapping Near-Circular Objects. In: Blanc-Talon, J., Kleihorst, R., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2011. Lecture Notes in Computer Science, vol 6915. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23687-7_16
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DOI: https://doi.org/10.1007/978-3-642-23687-7_16
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