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.
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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
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DOI: https://doi.org/10.1007/978-3-642-13803-4_49
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