@article {1144, title = {Artificial intelligence methods in discrete tomography}, year = {2012}, month = {June 2012}, pages = {16}, publisher = {University Szeged, Institute of Informatics}, type = {Abstract}, address = {Szeged}, abstract = {

Tomography is an imaging procedure to examine the internal structure of objects. The crosssection
images are constructed with the aid of the object{\textquoteright}s projections. It is often necessary to
minimize the number of those projections to avoid the damage or destruction of the examined
object, since in most cases the projections are made by destructive rays.
Sometimes the number of available projections are so small that conventional methods cannot
provide satisfactory results. In these cases Discrete Tomograpy can provide acceptable solutions,
but it can only be used with the assumption the object is made of only a few materials,
thus only a small number of intensity values appear in the reconstructed cross-section image.
Although there are a lot of discrete tomographic reconstruction algorithms, only a few papers
deal with the determination of intensity values of the image, in advance. In our work we
try to fill this gap by using different learning methods. During the learning and classification
we used the projection values as input arguments.
In the second part of our talk we concentrate on Binary Tomography (a special kind of Discrete
Tomography)where it is supposed that the object is composed of onematerial. Thus, there
can be only two intensities on the cross-section image - one for the object points and one for
the background. Here, we compared our earlier presented binary tomographic evolutionary
reconstruction algorithm to two others. We present the details of the above-mentioned reconstruction
method and our experimental results. This paper is based on our previous works.

}, author = {Mih{\'a}ly Gara and P{\'e}ter Bal{\'a}zs} } @inbook {1131, title = {Machine learning as a preprocessing phase in discrete tomography}, booktitle = {Applications of Discrete Geometry and Mathematical Morphology (WADGMM)}, series = {Lecture Notes in Computer Science}, number = {7346}, year = {2012}, note = {ScopusID: 84865454250doi: 10.1007/978-3-642-32313-3_8}, month = {Aug 2012}, pages = {109 - 124}, publisher = {Springer Verlag}, organization = {Springer Verlag}, type = {Conference paper}, address = {Berlin; Heidelberg; New York; London; Paris; Tokyo}, abstract = {

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. {\textcopyright} 2012 Springer-Verlag.

}, doi = {10.1007/978-3-642-32313-3_8}, author = {Mih{\'a}ly Gara and Tam{\'a}s S{\'a}muel Tasi and P{\'e}ter Bal{\'a}zs}, editor = {Ullrich K{\"o}the and Annick Montanvert and Pierre Soille} } @conference {1128, title = {Bin{\'a}ris tomogr{\'a}fiai rekonstrukci{\'o} objektum alap{\'u} evol{\'u}ci{\'o}s algoritmussal}, booktitle = {A K{\'e}pfeldolgoz{\'o}k {\'e}s Alakfelismer{\H o}k T{\'a}rsas{\'a}g{\'a}nak konferenci{\'a}ja - K{\'E}PAF 2011}, year = {2011}, month = {Jan 2011}, pages = {117 - 127}, publisher = {NJSZT}, organization = {NJSZT}, type = {Conference paper}, address = {Szeged}, author = {Mih{\'a}ly Gara and P{\'e}ter Bal{\'a}zs}, editor = {Zoltan Kato and K{\'a}lm{\'a}n Pal{\'a}gyi} } @conference {1121, title = {Binary tomographic reconstruction with an object-based evolutionary algorithm}, booktitle = {Conference of PhD Students in Computer Science. Volume of Extended Abstracts}, year = {2010}, month = {June 2010}, pages = {31}, publisher = {University of Szeged}, organization = {University of Szeged}, type = {Abstract}, address = {Szeged}, author = {Mih{\'a}ly Gara and P{\'e}ter Bal{\'a}zs} } @inbook {1125, title = {Machine learning for supporting binary tomographic reconstruction}, booktitle = {Workshop on Applications of Discrete Geometry in Mathematical Morphology}, series = {Lecture Notes in Computer Science}, year = {2010}, month = {Aug 2010}, pages = {101 - 105}, publisher = {Springer}, organization = {Springer}, type = {Conference paper}, address = {Istambul, Turkey}, author = {P{\'e}ter Bal{\'a}zs and Mih{\'a}ly Gara and Tam{\'a}s S{\'a}muel Tasi}, editor = {Ullrich K{\"o}the and Annick Montanvert and Pierre Soille} } @conference {1117, title = {D{\"o}nt{\'e}si f{\'a}kon alapul{\'o} el{\H o}feldolgoz{\'a}s a bin{\'a}ris tomogr{\'a}fi{\'a}ban}, booktitle = {A K{\'e}pfeldolgoz{\'o}k {\'e}s Alakfelismer{\H o}k T{\'a}rsas{\'a}g{\'a}nak konferenci{\'a}ja - K{\'E}PAF 2009}, year = {2009}, note = {8 pages}, month = {Jan 2009}, pages = {nincs sz{\'a}moz{\'a}s}, publisher = {Akaprint}, organization = {Akaprint}, type = {Conference paper}, address = {Budapest}, author = {Mih{\'a}ly Gara and P{\'e}ter Bal{\'a}zs}, editor = {Dmitrij Chetverikov and Tamas Sziranyi} } @inbook {1135, title = {An evolutionary approach for object-based image reconstruction using learnt priors}, booktitle = {Image Analysis}, series = {Lecture Notes in Computer Science}, number = {5575}, year = {2009}, note = {UT: 000268661000053ScopusID: 70350650400doi: 10.1007/978-3-642-02230-2_53}, month = {June 2009}, pages = {520 - 529}, publisher = {Springer-Verlag}, organization = {Springer-Verlag}, type = {Conference paper}, address = {Oslo, Norway}, abstract = {

In this paper we present a novel algorithm for reconstructingbinary images containing objects which can be described by some parameters. In particular, we investigate the problem of reconstructing binary images representing disks from four projections. We develop a genetic algorithm for this and similar problems. We also discuss how prior information on the number of disks can be incorporated into the reconstruction in order to obtain more accurate images. In addition, we present a method to exploit such kind of knowledge from the projections themselves. Experiments on artificial data are also conducted. {\textcopyright} 2009 Springer Berlin Heidelberg.

}, isbn = {978-3-642-02229-6}, doi = {10.1007/978-3-642-02230-2_53}, author = {P{\'e}ter Bal{\'a}zs and Mih{\'a}ly Gara}, editor = {Arnt-Borre Salberg and Jon Yngve Hardeberg and Robert Jenssen} } @article {1106, title = {Learning connectedness and convexity of binary images from their projections}, journal = {PURE MATHEMATICS AND APPLICATIONS}, volume = {20}, year = {2009}, month = {2009}, pages = {27 - 48}, type = {Journal article}, isbn = {1218-4586}, author = {Mih{\'a}ly Gara and Tam{\'a}s S{\'a}muel Tasi and P{\'e}ter Bal{\'a}zs} } @booklet {1283, title = {Supervised Color Image Segmentation in a Markovian Framework}, year = {2009}, month = {2009///}, abstract = {

This is the sample implementation of a Markov random field based color image segmentation algorithm described in the following paper: Zoltan Kato, Ting Chuen Pong, and John Chung Mong Lee. Color Image Segmentation and Parameter Estimation in a Markovian Framework. Pattern Recognition Letters, 22(3-4):309--321, March 2001. Note that the current demo program implements only a supervised version of the segmentation method described in the above paper (i.e. parameter values are learned interactively from representative regions selected by the user). Otherwise, the program implements exactly the color MRF model proposed in the paper. Images are automatically converted from RGB to the perceptually uniform CIE-L*u*v* color space before segmentation.

}, url = {http://www.inf.u-szeged.hu/~kato/software/colormrfdemo.html}, author = {Mih{\'a}ly Gara and Zoltan Kato} } @conference {1115, title = {Determination of geometric features of binary images from their projections by using decision trees}, booktitle = {Conference of PhD Students in Computer Science. Volume of Extended Abstracts}, year = {2008}, month = {July 2008}, pages = {26}, publisher = {University of Szeged}, organization = {University of Szeged}, type = {Abstract}, address = {Szeged, Hungary}, author = {Mih{\'a}ly Gara and P{\'e}ter Bal{\'a}zs}, editor = {K{\'a}lm{\'a}n Pal{\'a}gyi and Bal{\'a}zs B{\'a}nhelyi and Tam{\'a}s Gergely and Istv{\'a}n Matievics} }