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Automatic range image registration in the Markov chain.

Yonghuai Liu1

  • 1Aberystwyth University, Ceredigion, UK. yyl@aber.ac.uk

IEEE Transactions on Pattern Analysis and Machine Intelligence
|November 21, 2009
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel entropy for probability distributions, enhancing range image registration. The new method improves accuracy by modeling outliers and using mean field annealing for optimization.

Area of Science:

  • Thermodynamics
  • Computer Vision
  • Probability Theory

Background:

  • Traditional methods struggle with non-Gaussian distributions and outliers in range image registration.
  • Existing algorithms often fail to account for occlusions and viewpoint changes effectively.

Purpose of the Study:

  • To derive a novel entropy measure applicable to systems beyond the thermodynamic limit.
  • To develop an improved range image registration algorithm using this novel entropy.
  • To enhance the robustness and accuracy of camera motion parameter estimation.

Main Methods:

  • Derivation of a new entropy from Lyapunov functions for Markov chains.
  • Maximization of the novel entropy for probability estimation in range image correspondence.
  • Modeling the iterative registration process as a Markov chain with learned probabilities.

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  • Explicit modeling of outliers to handle occlusions and point set discrepancies.
  • Integration with mean field annealing for global optimization and weighted least-squares estimation.

    • Main Results:

    • The proposed entropy effectively describes both long and short-tailed probability distributions.
    • The novel registration algorithm demonstrates superior performance compared to ICP variants and genetic algorithms.
    • Accurate estimation of camera motion parameters is achieved even with significant outliers and occlusions.

    Conclusions:

    • The developed entropy provides a robust framework for probability estimation in complex systems.
    • The novel approach significantly advances the state-of-the-art in automatic overlapping range image registration.
    • This work offers a more reliable method for estimating camera motion parameters in challenging real-world scenarios.