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A hierarchical RBF online learning algorithm for real-time 3-D scanner.

Stefano Ferrari1, Francesco Bellocchio, Vincenzo Piuri

  • 1Department of Information Technology, Università degli Studi di Milano, Crema, Italy. stefano.ferrari@unimi.it

IEEE Transactions on Neural Networks
|December 17, 2009
PubMed
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A new real-time online network model, derived from the hierarchical radial basis function (HRBF) model, enables adaptive learning for 3-D scanning. This model achieves accuracy comparable to batch methods while supporting continuous data acquisition.

Area of Science:

  • Computational geometry
  • Machine learning
  • Computer vision

Background:

  • Traditional batch learning methods struggle with real-time data acquisition.
  • Hierarchical Radial Basis Function (HRBF) models offer a basis for complex surface representation.
  • Real-time adaptation is crucial for interactive applications like 3-D scanning.

Purpose of the Study:

  • To introduce a novel real-time online network model.
  • To enable adaptive learning and surface detail capture during data collection.
  • To apply and validate the model in the context of 3-D scanning.

Main Methods:

  • The model is derived from the hierarchical radial basis function (HRBF) approach.
  • It features automatic unit addition at finer scales for surface detail.

Related Experiment Videos

  • A quad-tree structure supports local network reconfiguration for real-time performance.

    • Main Results:

    • The model demonstrates real-time online learning capabilities.
    • Accuracy in 3-D scanning is comparable to batch HRBF and Support Vector Machines (SVMs).
    • The model provides real-time manifold display for guiding 3-D acquisition.

    Conclusions:

    • The proposed real-time online network model is effective for 3-D scanning.
    • It overcomes the limitations of batch approaches for online learning scenarios.
    • Convergence of the model is mathematically proven.