Abstract
We consider binary images such that an image includes a single 2-D shape, from which we extract three populations of three different (shape) descriptors, respectively. Each population is represented by an Intervals’ Number, or IN for short, in the mathematical lattice (F, ≼ ) of INs. In conclusion, a 2-D shape is represented in the Cartesian product lattice (F 3, ≼ ). We present a 2-D shape classification scheme based on fuzzy lattice reasoning (FLR). Preliminary experimental results have been encouraging. We discuss the potential of Lattice Computing (LC) techniques in image representation and recognition applications.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Amanatiadis, A., Kaburlasos, V.G., Gasteratos, A., Papadakis, S.E.: A comparative study of invariant descriptors for shape retrieval. In: Proc. 2009 IEEE Intl. Conf. on Imaging Systems & Techniques (IST 2009), pp. 391–394 (2009)
Graña, M.: State of the art in lattice computing for artificial intelligence applications. In: Nadarajan, R., Anitha, R., Porkodi, C. (eds.) Mathematical and Computational Models, pp. 233–242 (2007)
Graña, M.: Lattice computing: lattice-theory-based computational intelligence. In: Matsuhisa, T., Koibuchi, H. (eds.) Proc. Kosen Workshop on Mathematics, Technology, and Education (MTE), pp. 19–27 (2008)
Kaburlasos, V.G.: Towards a Unified Modeling and Knowledge-Representation Based on Lattice Theory. SCI, vol. 27. Springer, Heidelberg (2006)
Kaburlasos, V.G.: Granular fuzzy inference system (FIS) design by lattice computing. In: Corchado Rodriguez, E.S., et al. (eds.) HAIS 2010, Part II. LNCS (LNAI), vol. 6077, pp. 410–417. Springer, Heidelberg (2010)
Kaburlasos, V.G., Papadakis, S.E.: A granular extension of the fuzzy-ARTMAP (FAM) neural classifier based on fuzzy lattice reasoning (FLR). Neurocomputing 72(10-12), 2067–2078 (2009)
Papadakis, S.E., Kaburlasos, V.G.: Induction of classification rules from histograms. In: Proc. 8th Intl. Conf. on Natural Computing, Joint Conf. on Information Sciences (JCIS 2007), pp. 1646–1652 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kaburlasos, V.G., Amanatiadis, A., Papadakis, S.E. (2010). 2-D Shape Representation and Recognition by Lattice Computing Techniques. In: Corchado, E., Graña Romay, M., Manhaes Savio, A. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13803-4_49
Download citation
DOI: https://doi.org/10.1007/978-3-642-13803-4_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13802-7
Online ISBN: 978-3-642-13803-4
eBook Packages: Computer ScienceComputer Science (R0)