A Local Adaptation of the Histogram Radon Transform Descriptor: An Application to a Shoe Print Dataset

Makoto Hasegawa 1 Salvatore Tabbone 1
1 QGAR - Querying Graphics through Analysis and Recognition
LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
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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Chapitre d'ouvrage
Georgy Gimel'farb. Structural, Syntactic, and Statistical Pattern Recognition: Lecture Notes in Computer Science, 7626, Springer, pp.675-683, 2012, Lecture Notes in Computer Science, 978-3-642-34165-6. 〈10.1007/978-3-642-34166-3_74〉
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https://hal.inria.fr/hal-00764901
Contributeur : Makoto Hasegawa <>
Soumis le : jeudi 13 décembre 2012 - 15:53:45
Dernière modification le : jeudi 11 janvier 2018 - 06:25:25

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Makoto Hasegawa, Salvatore Tabbone. A Local Adaptation of the Histogram Radon Transform Descriptor: An Application to a Shoe Print Dataset. Georgy Gimel'farb. Structural, Syntactic, and Statistical Pattern Recognition: Lecture Notes in Computer Science, 7626, Springer, pp.675-683, 2012, Lecture Notes in Computer Science, 978-3-642-34165-6. 〈10.1007/978-3-642-34166-3_74〉. 〈hal-00764901〉

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