Online parameter estimation in dynamic Markov Random Fields for image sequence analysis

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Publication Type pre-print
School or College School of Medicine
Department Ophthalmology
Creator Jones, Bryan W.
Other Author Jagadeesh, Vignesh; Manjunath, B. S.; Anderson, James; Marc, Robert; Fisher, Steven K.
Title Online parameter estimation in dynamic Markov Random Fields for image sequence analysis
Date 2012-01-01
Description Markov Random Fields (MRF) have proven to be extremely useful models for efficient and accurate image segmentation.Recent literature points to an increased effort towards incorporating useful priors (shape, geometry, context) in a MRF framework. However, topological priors, considered extremely crucial in biological and natural image sequences have been less explored. This work proposes a strategy wherein free parameters of the MRF are used to make it topology aware using a semantic graphical model working in conjunction with the MRF. Estimation of free parameters is constrained by prior knowledge of an object's topological dynamics encoded by the graphical model. Maximizing a regional conformance measure yields parameters for the frame under consideration. The application motivating this work is the tracing of neuronal structures across 3D serial section Transmission Electron Micrograph (ssTEM) stacks. Applicability of the proposed method is demonstrated by tracing 3D structures in ssTEM stacks.
Type Text
Publisher Institute of Electrical and Electronics Engineers (IEEE)
First Page 301
Last Page 304
Language eng
Bibliographic Citation Jagadeesh, V., Manjunath, B. S., Anderson, J., Jones, B., Marc, R., & Fisher, S. K. (2012). Online parameter estimation in dynamic Markov Random Fields for image sequence analysis. IEEE PES Innovative Smart Grid Technologies Conference Europe, no. 6466855, 301-4.
Rights Management (c) 2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Format Medium application/pdf
Format Extent 2,991,344 bytes
Identifier uspace,18210
ARK ark:/87278/s61z4p6z
Setname ir_uspace
Date Created 2013-04-01
Date Modified 2013-04-01
ID 708351
Reference URL https://collections.lib.utah.edu/ark:/87278/s61z4p6z
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