In this paper, Hidden Markov Models (HMMs) are investigated for thepurpose of classifying planar shapes represented by their curvature coefficients. In the training phase, special attention is devoted to the initialization and model selection issues, which make the learning phase particularly effective. The results of tests on different data sets show that the proposed system is able to accurately classify objects that were translated, rotated, occluded, or deformed by shearing, also in the presence of noise.

Investigating Hidden Markov Models' Capabilities in 2D Shape Classification

BICEGO, Manuele;MURINO, Vittorio
2004-01-01

Abstract

In this paper, Hidden Markov Models (HMMs) are investigated for thepurpose of classifying planar shapes represented by their curvature coefficients. In the training phase, special attention is devoted to the initialization and model selection issues, which make the learning phase particularly effective. The results of tests on different data sets show that the proposed system is able to accurately classify objects that were translated, rotated, occluded, or deformed by shearing, also in the presence of noise.
2004
Pattern Recognition; Shape analysis; probabilistic graphical models
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/301392
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