TY - CHAP T1 - Machine learning as a preprocessing phase in discrete tomography T2 - Applications of Discrete Geometry and Mathematical Morphology (WADGMM) Y1 - 2012 A1 - Mihály Gara A1 - Tamás Sámuel Tasi A1 - Péter Balázs ED - Ullrich Köthe ED - Annick Montanvert ED - Pierre Soille AB -

In this paper we investigate for two well-known machine learning methods, decision trees and neural networks, how they classify discrete images from their projections. As an example, we present classification results when the task is to guess the number of intensity values of the discrete image. Machine learning can be used in Discrete Tomography as a preprocessing step in order to choose the proper reconstruction algorithm or - with the aid of the knowledge acquired - to improve its accuracy. We also show how to design new evolutionary reconstruction methods that can exploit the information gained by machine learning classifiers. © 2012 Springer-Verlag.

JF - Applications of Discrete Geometry and Mathematical Morphology (WADGMM) T3 - Lecture Notes in Computer Science PB - Springer Verlag CY - Berlin; Heidelberg; New York; London; Paris; Tokyo N1 - ScopusID: 84865454250doi: 10.1007/978-3-642-32313-3_8 JO - LNCS ER -