Model-Based Estimation Techniques for 3-D Reconstruction from Projections
dc.contributor.author | Bresler, Yoram | en_US |
dc.contributor.author | Fessler, Jeffrey A. | en_US |
dc.contributor.author | Macovski, Albert | en_US |
dc.date.accessioned | 2011-08-18T18:21:10Z | |
dc.date.available | 2011-08-18T18:21:10Z | |
dc.date.issued | 1988-06 | en_US |
dc.identifier.citation | Bresler, Y.; Fessler, J. A.; Macovski, A. (1988). "Model-Based Estimation Techniques for 3-D Reconstruction from Projections." Machine Vision and Applications 1(2): 115-126. <http://hdl.handle.net/2027.42/85941> | en_US |
dc.identifier.issn | 0932-8092; 1432-1769 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/85941 | |
dc.description.abstract | A parametric estimation approach to reconstruction from projections with incomplete and very noisy data is described. Embedding prior knowledge about "objects" in the probed domain and about the data acquisition process into stochastic dynamic models, we transform the reconstruction problem into a computationally ,challenging nonlinear state-estimation problem, where the objects' parametrized descriptions are to be directly estimated from the projection data. This paper is a review in a common framework and a comparative study of two distinct algorithms which were developed recently for the solution of this problem. The first, is an approximate, globally optimal minimum-meansquare- error recursive algorithm. The second is a hierarchical suboptimal Bayesian algorithm. Simulation examples demonstrate accurate reconstructions with as few as four views in a 135 ~ sector, at an average signal to noise ratio of 0.6. | en_US |
dc.publisher | Springer | en_US |
dc.title | Model-Based Estimation Techniques for 3-D Reconstruction from Projections | en_US |
dc.type | article | en_US |
dc.subject.hlbsecondlevel | Biomedical Engineering | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationother | Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL 61821 USA. Information Systems Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA 94305 USA. | en_US |
dc.identifier.pmid | 23074356 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/85941/1/Fessler114.pdf | |
dc.identifier.doi | 10.1007/BF01212276 | en_US |
dc.identifier.source | Machine Vision and Applications | en_US |
dc.owningcollname | Electrical Engineering and Computer Science, Department of (EECS) |
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