A multi-layer `gas of circles' Markov random field model for the extraction of overlapping near-circular objects

Nemeth, J.; Kato, Z.; Jermyn, I.H.

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.

Citation

Nemeth, J., Kato, Z., & Jermyn, I. (2011, August). A multi-layer `gas of circles' Markov random field model for the extraction of overlapping near-circular objects. Presented at 13th International Conference Advanced Concepts for Intelligent Vision Systems (ACIVS 2011), Ghent

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 13th International Conference Advanced Concepts for Intelligent Vision Systems (ACIVS 2011)
• Publication Date: Aug 1, 2011
• Deposit Date: Aug 12, 2011
• Publicly Available Date: Apr 13, 2016
• Print ISSN: 0302-9743
• Pages: 171-182
• Series Title: Lecture notes in computer science
• Series Number: 8192
• Series ISSN: 0302-9743
• Book Title: Advanced Concepts for Intelligent Vision Systems: 13th International Conference, ACIVS 2011, Ghent, Belgium, August 22-25, 2011, proceedings
• ISBN: 9783642236860
• DOI: https://doi.org/10.1007/978-3-642-23687-7_16
• Public URL: https://durham-repository.worktribe.com/output/1158096