Abstract
In this paper we propose a shape recognition approach applied to a dataset composed of 512 shoeprints where shapes are strongly occluded. We provide a local adaptation of the HRT (Histogram Radon Transform) descriptor. A shoeprint is decomposed into its connect components and describes locally by the local HRT. Then, following this description, we find the best local matching between the connected components and the similarity between two images is defined as mean of local similarity measures.
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© 2012 Springer-Verlag Berlin Heidelberg
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Hasegawa, M., Tabbone, S. (2012). A Local Adaptation of the Histogram Radon Transform Descriptor: An Application to a Shoe Print Dataset. In: Gimel’farb, G., et al. Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2012. Lecture Notes in Computer Science, vol 7626. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34166-3_74
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DOI: https://doi.org/10.1007/978-3-642-34166-3_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34165-6
Online ISBN: 978-3-642-34166-3
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