Uncertainty estimation in reconstructed deformable models
One of the hallmarks of the Bayesian approach to modeling is the posterior probability, which summarizes all uncertainties regarding the analysis. Using a Markov Chain Monte Carlo (MCMC) technique, it is possible to generate a sequence of objects that represent random samples drawn from the posterior distribution. We demonstrate this technique for reconstructions of two-dimensional objects from noisy projections taken from two directions. The reconstructed object is modeled in terms of a deformable geometrically-defined boundary with a constant interior density yielding a nonlinear reconstruction problem. We show how an MCMC sequence can be used to estimate uncertainties in the location of the edge of the reconstructed object.
- Research Organization:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- W-7405-ENG-36
- OSTI ID:
- 459819
- Report Number(s):
- LA-UR-96-4437; CONF-9608189-1; ON: DE97003134
- Resource Relation:
- Conference: Workshop on maximum entropy and bayesian methods, Kruger National Park (South Africa), 13-16 Aug 1996; Other Information: PBD: [1996]
- Country of Publication:
- United States
- Language:
- English
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