Abstract
Cyclic strings are sequences with no beginning or end, which are useful for modelling different objects in pattern recognition. For instance, in digital image analysis, strings can represent contours of 2D objects. Several methods have been described to compute the dissimilarity of two cyclic strings such as the cyclic edit distance. However, no method has been provided that takes into account the normalization of the cyclic edit distance. In this paper, we propose an algorithm to compute normalized cyclic edit distances, and illustrate the performance of the method in practice.
This work has been supported by the the Spanish Ministerio de Ciencia y Tecnología and FEDER under grant TIC2002-02684.
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© 2004 Springer-Verlag Berlin Heidelberg
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Marzal, A., Peris, G. (2004). Normalized Cyclic Edit Distances: An Efficient Algorithm. In: Conejo, R., Urretavizcaya, M., Pérez-de-la-Cruz, JL. (eds) Current Topics in Artificial Intelligence. TTIA 2003. Lecture Notes in Computer Science(), vol 3040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25945-9_43
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DOI: https://doi.org/10.1007/978-3-540-25945-9_43
Publisher Name: Springer, Berlin, Heidelberg
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