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Bicego, Manuele and Trudda, Alessandro (2008) 2D shape classification using multifractional Brownian motion. In: Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop, SSPR & SPR 2008: proceedings, 4-6 December 2008, Orlando, USA. Berlin - Heidelberg, Springer. p. 906-916. (Lecture Notes in Computer Science, 5342/2008). ISBN 978-3-540-89688-3. Conference or Workshop Item. Full text not available from this repository. DOI: 10.1007/978-3-540-89689-0_94 AbstractIn this paper a novel approach to contour-based 2D shape recognition is proposed. The main idea is to characterize the contour of an object using the multifractional Brownian motion (mBm), a mathematical method able to capture the local self similarity and long-range dependence of a signal. The mBm estimation results in a sequence of Hurst coefficients, which we used to derive a fixed size feature vector. Preliminary experimental evaluations using simple classifiers with these feature vectors produce encouraging results, also in comparison with the state of the art.
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