Abstract
We consider the problem of reconstructing binary images from their row and column sums with prescribed number of strips in each row and column. In a previous paper we compared an exact deterministic and an approximate stochastic method (Simulated Annealing – SA) to solve the problem. We found that the latter one is much more suitable for practical purposes. Since SA is sensitive to the choice of the initial state, in this paper we present different strategies for choosing a starting image, and thus we develop variants of the SA method for strip constrained binary tomography. We evaluate the different approaches on images with varying densities of object pixels.
This research was supported by the project “Integrated program for training new generation of scientists in the fields of computer science”, no EFOP-3.6.3-VEKOP-16-2017-0002. The project has been supported by the European Union and co-funded by the European Social Fund.
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Szűcs, J., Balázs, P. (2019). Variants of Simulated Annealing for Strip Constrained Binary Tomography. In: Barneva, R., Brimkov, V., Kulczycki, P., Tavares, J. (eds) Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications. CompIMAGE 2018. Lecture Notes in Computer Science(), vol 10986. Springer, Cham. https://doi.org/10.1007/978-3-030-20805-9_8
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