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Related Concept Videos

Carbon Skeletons01:12

Carbon Skeletons

Life on Earth is carbon-based, as all macromolecules that make up living organisms contain carbon atoms. All organic compounds have a carbon backbone. Each carbon atom is tetravalent and can bond with four other atoms, making it an extraordinarily flexible component of biological molecules. Because carbon’s valence electrons are stable, it rarely becomes an ion. As the carbon chain increases in length, structural modifications such as ring structures, double bonds, and branching side chains...
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The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The long...
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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

Bayesian estimation of the shape skeleton.

Jacob Feldman1, Manish Singh

  • 1Department of Psychology, Center for Cognitive Science, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA. jacob@ruccs.rutgers.edu

Proceedings of the National Academy of Sciences of the United States of America
|November 15, 2006
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Bayesian probabilistic approach for robust shape skeleton computation. It effectively identifies shape skeletons even with noisy contours, overcoming limitations of traditional geometric methods.

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Area of Science:

  • Computational geometry
  • Computer vision
  • Biomedical imaging

Background:

  • Shape skeletons offer compact representations for neural modeling and computation.
  • Existing geometric methods for skeleton computation are sensitive to noise and produce counterintuitive results.
  • A robust and intuitive method for shape skeletonization is needed.

Purpose of the Study:

  • To develop a robust Bayesian probabilistic approach for shape skeleton computation.
  • To overcome the limitations of conventional geometric methods in handling noisy shapes.
  • To provide a skeletal representation that corresponds to natural shape parts.

Main Methods:

  • Introduced a Bayesian probabilistic framework for shape skeletonization.
  • Modeled shapes as arising from a stochastic generative process originating from a skeleton.
  • Employed Bayesian estimation to find the maximum a posteriori (MAP) skeleton.

Main Results:

  • The proposed Bayesian approach yields robust skeletal representations.
  • The method effectively handles natural shapes with significant contour noise.
  • Skeletal branches accurately correspond to the natural parts of the shape.

Conclusions:

  • The Bayesian probabilistic approach offers a significant improvement over traditional methods for shape skeletonization.
  • This method provides a more intuitive and reliable way to extract shape structure.
  • The technique has potential applications in areas requiring robust shape analysis.