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Proceedings Paper

Discrete mathematics for spatial data classification and understanding
Author(s): Luigi Mussio; Rossella Nocera; Daniela Poli
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Paper Abstract

Data processing, in the field of information technology, requires new tools, involving discrete mathematics, like data compression, signal enhancement, data classification and understanding, hypertexts and multimedia (considering educational aspects too), because the mass of data implies automatic data management and doesn't permit any a priori knowledge. The methodologies and procedures used in this class of problems concern different kinds of segmentation techniques and relational strategies, like clustering, parsing, vectorization, formalization, fitting and matching. On the other hand, the complexity of this approach imposes to perform optimal sampling and outlier detection just at the beginning, in order to define the set of data to be processed: rough data supply very poor information. For these reasons, no hypotheses about the distribution behavior of the data can be generally done and a judgment should be acquired by distribution-free inference only.

Paper Details

Date Published: 14 December 1998
PDF: 10 pages
Proc. SPIE 3641, Videometrics VI, (14 December 1998); doi: 10.1117/12.333786
Show Author Affiliations
Luigi Mussio, Politecnico di Milano (Italy)
Rossella Nocera, Univ. degli Studi di Reggio Calabria (Switzerland)
Daniela Poli, Politecnico di Milano (Italy)

Published in SPIE Proceedings Vol. 3641:
Videometrics VI
Sabry F. El-Hakim; Armin Gruen, Editor(s)

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