An Evolutionary Approach for Object-Based Image Reconstruction Using Learnt Priors

Authors:
Péter Balázs;Mihály Gara
Affiliations:
Department of Image Processing and Computer Graphics, University of Szeged, Szeged, Hungary H-6720;Department of Image Processing and Computer Graphics, University of Szeged, Szeged, Hungary H-6720
Venue:
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Year:
2009

Citing 7
Cited 5

C4.5: programs for machine learning

C4.5: programs for machine learning
Image reconstruction and correction methods in neutron and X-ray tomography

Acta Cybernetica
Advances in Discrete Tomography and Its Applications (Applied and Numerical Harmonic Analysis)

Advances in Discrete Tomography and Its Applications (Applied and Numerical Harmonic Analysis)
A genetic algorithm for discrete tomography reconstruction

Genetic Programming and Evolvable Machines
Decision Trees in Binary Tomography for Supporting the Reconstruction of hv-Convex Connected Images

ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
An evolutionary algorithm for discrete tomography

Discrete Applied Mathematics - Special issue: IWCIA 2003 - Ninth international workshop on combinatorial image analysis
A memetic algorithm for binary image reconstruction

IWCIA'08 Proceedings of the 12th international conference on Combinatorial image analysis

Discrete tomography reconstruction based on the multi-well potential

IWCIA'11 Proceedings of the 14th international conference on Combinatorial image analysis
A memetic island model for discrete tomography reconstruction

WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
Direction-dependency of binary tomographic reconstruction algorithms

Graphical Models
Projection selection dependency in binary tomography

Acta Cybernetica
An island strategy for memetic discrete tomography reconstruction

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we present a novel algorithm for reconstructing binary 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.