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

Manuele Bicego1, Vittorio Murino

  • 1Dipartimento di Informatica, Università di Verona, Ca' Vignal 2, Strada Le Grazie 15, 37134 Verona, Italy. bicego@sci.univr.it

IEEE Transactions on Pattern Analysis and Machine Intelligence
|September 21, 2004
PubMed
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Hidden Markov Models (HMMs) effectively classify planar shapes using curvature coefficients. This robust method accurately identifies shapes despite transformations, occlusions, deformations, and noise.

Area of Science:

  • Computer Vision
  • Pattern Recognition
  • Machine Learning

Background:

  • Classifying planar shapes is crucial in various fields.
  • Existing methods may struggle with transformations and noise.
  • Hidden Markov Models (HMMs) offer a probabilistic framework for sequence analysis.

Purpose of the Study:

  • To investigate the efficacy of Hidden Markov Models (HMMs) for planar shape classification.
  • To address challenges in HMM training, specifically initialization and model selection.
  • To evaluate the performance of the proposed HMM-based system on diverse datasets.

Main Methods:

  • Representing planar shapes using curvature coefficients.
  • Employing Hidden Markov Models (HMMs) for classification.

Related Experiment Videos

  • Focusing on effective initialization and model selection strategies during training.

    • Main Results:

    • The HMM-based system achieved accurate classification of planar shapes.
    • The system demonstrated robustness against translation, rotation, occlusion, and shearing deformations.
    • Performance remained high even in the presence of noise.

    Conclusions:

    • Hidden Markov Models (HMMs) provide a powerful tool for planar shape classification.
    • Effective training strategies significantly enhance HMM performance.
    • The proposed method offers a reliable solution for shape recognition in challenging conditions.