### Abstract

Discrete tomography concerns the reconstruction of functions with a finite number of values from few projections. For a number of important real-world problems, this tomography problem involves thousands of variables. Applicability and performance of discrete tomography therefore largely depend on the criteria used for reconstruction and the optimization algorithm applied. From this viewpoint, we evaluate two major optimization strategies, simulated annealing and convex-concave regularization, for the case of binary-valued functions using various data sets. Extensive numerical experiments show that despite being quite different from the viewpoint of optimization, both strategies show similar reconstruction performance as well as robustness to noise.

Original language | English |
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Title of host publication | Discrete Geometry for Computer Imagery - 13th International Conference, DGCI 2006, Proceedings |

Publisher | Springer Verlag |

Pages | 146-156 |

Number of pages | 11 |

ISBN (Print) | 3540476512, 9783540476511 |

Publication status | Published - Jan 1 2006 |

Event | 13th International Conference on Discrete Geometry for Computer Imagery, DGCI 2006 - Szeged, Hungary Duration: Oct 25 2006 → Oct 27 2006 |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 4245 LNCS |

ISSN (Print) | 0302-9743 |

ISSN (Electronic) | 1611-3349 |

### Other

Other | 13th International Conference on Discrete Geometry for Computer Imagery, DGCI 2006 |
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Country | Hungary |

City | Szeged |

Period | 10/25/06 → 10/27/06 |

### ASJC Scopus subject areas

- Theoretical Computer Science
- Computer Science(all)

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## Cite this

*Discrete Geometry for Computer Imagery - 13th International Conference, DGCI 2006, Proceedings*(pp. 146-156). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4245 LNCS). Springer Verlag.