{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,10,24]],"date-time":"2023-10-24T05:40:27Z","timestamp":1698126027282},"reference-count":23,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2005,10,20]],"date-time":"2005-10-20T00:00:00Z","timestamp":1129766400000},"content-version":"vor","delay-in-days":4067,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int J Imaging Syst Tech"],"published-print":{"date-parts":[[1994,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The problem of edge\u2010preserving tomographic reconstruction from Gaussian data is considered. The problem is formulated within a Bayesian framework, where the image is modeled as a pair of Markov Random Fields: a continuous\u2010valued intensity process and a binary line process. The <jats:italic>a priori<\/jats:italic> information considered here enforces constraints both on the local regularity of the image and on the line configurations. The solution, defined as the maximizer of the posterior probability, is obtained using a Generalized Expectation\u2010Maximization (GEM) algorithm, in which both the intensity and the line processes are iteratively updated. The simulation results show that introducing suitable priors on the line configurations improves the quality of the reconstructed images, and is particularly useful when the data record is small. The relationships with other approaches for managing discontinuities are outlined. A comparison between the GEM algorithm and an algorithm based on mixed\u2010annealing is made on the basis of computer simulations.\u00a91994 John Wiley &amp; Sons Inc<\/jats:p>","DOI":"10.1002\/ima.1850050306","type":"journal-article","created":{"date-parts":[[2007,3,5]],"date-time":"2007-03-05T23:10:41Z","timestamp":1173136241000},"page":"231-238","source":"Crossref","is-referenced-by-count":6,"title":["Edge\u2010preserving tomographic reconstruction from gaussian data using a gibbs prior and a generalized expectation\u2010maximization algorithm"],"prefix":"10.1002","volume":"5","author":[{"given":"Luigi","family":"Bedini","sequence":"first","affiliation":[]},{"given":"Emanuele","family":"Salerno","sequence":"additional","affiliation":[]},{"given":"Anna","family":"Tonazzini","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2005,10,20]]},"reference":[{"key":"e_1_2_1_2_2","first-page":"869","article-title":"Ill\u2010posed problems in early vision","volume":"64","author":"Bertero M.","year":"1984","journal-title":"IEEE Proc."},{"key":"e_1_2_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1984.4767596"},{"key":"e_1_2_1_4_2","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1080\/01621459.1987.10478393","article-title":"Probabilistic solution of ill\u2010posed problems in computational vision","volume":"82","author":"Marroquin J.","year":"1987","journal-title":"J. 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