View(s) :
1 (0 ULiège)
Download(s) :
0 (0 ULiège)
Discrete Tomography : a joint Contribution by Optimization, Equivariance Analysis and Learning
p. 3-26
Abstract
Optimization theory is a key technology for inverse problems of reconstruction with applications in science, technology and economy. Discrete tomography is a modern research field which deals with finite objects from VLSI chip design or medical imaging. This paper focuses on the utilization of modern optimization methods to approximately resolve the NP-hard reconstruction problem of discrete tomography. Our new approaches and introductions are based on modeling and algorithms from coding theory and optimal experimental design. Here, we combine continuous and discrete optimization with exploiting geometrical symmetries, or more generally, equivariances, in a framework of statistical learning.
Text
References
Bibliographical reference
Öznur Yaşar and Gerhard-Wilhelm Weber, « Discrete Tomography : a joint Contribution by Optimization, Equivariance Analysis and Learning », CASYS, 18 | 2006, 3-26.
Electronic reference
Öznur Yaşar and Gerhard-Wilhelm Weber, « Discrete Tomography : a joint Contribution by Optimization, Equivariance Analysis and Learning », CASYS [Online], 18 | 2006, Online since 30 July 2024, connection on 27 December 2024. URL : http://popups.uliege.be/3041-539x/index.php?id=2195
Authors
Öznur Yaşar
Department of Mathematics and Statistics, Memorial University of Newfoundland, St. John's, NL, Canada
Gerhard-Wilhelm Weber
Institute of Applied Mathematics, METU, Ankara, Turkey