Switching components, also named as bad configurations, interchanges, and ghosts (according to different scenarios), play a key role in the study of ambiguous configurations, which often appear in Discrete Tomography and in several other areas of research. In this paper we give an upper bound for the minimal size bad configurations associated to a given set $$S$$S of lattice directions. In the special but interesting case of four directions, we show that the general argument can be considerably improved, and we present an algebraic method which provides such an improvement. Moreover, it turns out that finding bad configurations is in fact equivalent to finding multiples of a suitable polynomial in two variables, having only coefficients from the set $$\{-1,0,1\}$${-1,0,1}. The general problem of describing all polynomials having such multiples seems to be very hard (Borwein and Erdélyi, in Ill J Math 41(4):667–675, 1997). However, in our particular case, it is hopeful to give some kind of solution. In the context of Digital Image Analysis, it represents an explicit method for the construction of ghosts, and consequently might be of interest in image processing, also in view of efficient algorithms to encode data.

Peri, C., Brunetti, S., Dulio, P., Hajdu, L., Ghosts in Discrete Tomography, <<JOURNAL OF MATHEMATICAL IMAGING AND VISION>>, 2015; 2015/53 (2): 210-224. [doi:10.1007/s10851-015-0571-2] [http://hdl.handle.net/10807/68675]

Ghosts in Discrete Tomography

Peri;Sara; Dulio;
2015

Abstract

Switching components, also named as bad configurations, interchanges, and ghosts (according to different scenarios), play a key role in the study of ambiguous configurations, which often appear in Discrete Tomography and in several other areas of research. In this paper we give an upper bound for the minimal size bad configurations associated to a given set $$S$$S of lattice directions. In the special but interesting case of four directions, we show that the general argument can be considerably improved, and we present an algebraic method which provides such an improvement. Moreover, it turns out that finding bad configurations is in fact equivalent to finding multiples of a suitable polynomial in two variables, having only coefficients from the set $$\{-1,0,1\}$${-1,0,1}. The general problem of describing all polynomials having such multiples seems to be very hard (Borwein and Erdélyi, in Ill J Math 41(4):667–675, 1997). However, in our particular case, it is hopeful to give some kind of solution. In the context of Digital Image Analysis, it represents an explicit method for the construction of ghosts, and consequently might be of interest in image processing, also in view of efficient algorithms to encode data.
Inglese
Peri, C., Brunetti, S., Dulio, P., Hajdu, L., Ghosts in Discrete Tomography, <<JOURNAL OF MATHEMATICAL IMAGING AND VISION>>, 2015; 2015/53 (2): 210-224. [doi:10.1007/s10851-015-0571-2] [http://hdl.handle.net/10807/68675]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/68675
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