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Authors: Mario D'Acunto 1 ; Gabriele Pieri 2 ; Marco Righi 2 and Ovidio Salvetti 2

Affiliations: 1 National Research Council and ISM-CNR, Italy ; 2 National Research Council and ISTI-CNR, Italy

Keyword(s): Enhanced Resolution; Image Analysis; Pattern Recognition, Artifacts and Noise, Atomic Force Microscopy

Abstract: Image acquisition systems integrated with laboratory automation produces multi-dimensional datasets. An effective computational approach to objectively analyzing image datasets is pattern recognition (PR), i.e. a machinelearning approach where the machine finds relevant patterns that distinguish groups of objects after being trained on examples (supervised machine learning). In contrast, the other approach to machine learning and artificial intelligence is unsupervised learning, where the intelligent process finds relevant patterns without relying on prior training examples, usually by using a set of pre-defined rules. In this paper we apply a method derived by usual PR techniques for the recognition of artifacts and noise on images recorded with Atomic Force Microscopy (AFM). The advantage of automatic artifacts recognition could be the implementation of machine learning languages for AFM investigations.

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Paper citation in several formats:
D'Acunto, M.; Pieri, G.; Righi, M. and Salvetti, O. (2013). Enhanced Resolution Methods for Improving Image Analysis and Pattern Recognition in Scanning Probe Microscopy. In Proceedings of the 4th International Workshop on Image Mining. Theory and Applications (VISIGRAPP 2013) - IMTA-4; ISBN 978-989-8565-50-1, SciTePress, pages 22-28. DOI: 10.5220/0004392400220028

@conference{imta-413,
author={Mario D'Acunto. and Gabriele Pieri. and Marco Righi. and Ovidio Salvetti.},
title={Enhanced Resolution Methods for Improving Image Analysis and Pattern Recognition in Scanning Probe Microscopy},
booktitle={Proceedings of the 4th International Workshop on Image Mining. Theory and Applications (VISIGRAPP 2013) - IMTA-4},
year={2013},
pages={22-28},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004392400220028},
isbn={978-989-8565-50-1},
}

TY - CONF

JO - Proceedings of the 4th International Workshop on Image Mining. Theory and Applications (VISIGRAPP 2013) - IMTA-4
TI - Enhanced Resolution Methods for Improving Image Analysis and Pattern Recognition in Scanning Probe Microscopy
SN - 978-989-8565-50-1
AU - D'Acunto, M.
AU - Pieri, G.
AU - Righi, M.
AU - Salvetti, O.
PY - 2013
SP - 22
EP - 28
DO - 10.5220/0004392400220028
PB - SciTePress

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