Investigating Hidden Markov Models' capabilities in 2D shape classification.

Manuele Bicego; Vittorio Murino

doi:10.1109/tpami.2004.1262200

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Investigating Hidden Markov Models' capabilities in 2D shape classification.

Manuele Bicego·Vittorio Murino

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Abstract

In this paper, Hidden Markov Models (HMMs) are investigated for the purpose 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.

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Algorithms Artificial Intelligence Computer Graphics Computer Simulation Image Enhancement Image Interpretation, Computer-Assisted Information Storage and Retrieval Markov Chains Models, Statistical Numerical Analysis, Computer-Assisted Pattern Recognition, Automated Reproducibility of Results Sensitivity and Specificity Signal Processing, Computer-Assisted Subtraction Technique Comparative Study Evaluation Study Journal Article Validation Study

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Manuele Bicego, Vittorio Murino. 2004. Investigating Hidden Markov Models' capabilities in 2D shape classification.. https://doi.org/10.1109/tpami.2004.1262200

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