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Related Concept Videos

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Types of Errors: Detection and Minimization

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Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
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According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
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The sampling variability of a statistic is defined as how much the statistic varies from one sample to another. The sampling variability of a statistic is typically measured by measuring its standard error.
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Minimizing fiducial localization error using sphere-based registration in jaw tracking.

Amir H Abdi1, Alan G Hannam2, Ian K Stavness3

  • 1Electrical and Computer Engineering Department, University of British Columbia, Vancouver, Canada.

Journal of Biomechanics
|December 28, 2017
PubMed
Summary

This study introduces a novel sphere-based registration method to improve the accuracy of dental model tracking. The new method significantly reduces fiducial localization error (FLE) for better analysis of masticatory functions.

Keywords:
Dental occlusionFiducial localizationMandible trackingRegistration

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Area of Science:

  • Biomedical Engineering
  • Dental Technology
  • Computer-Aided Design/Manufacturing (CAD/CAM)

Background:

  • Current jaw tracking methods face limitations in accuracy and clinical use.
  • Effective analysis of masticatory functions requires precise tracking and coregistration of dental models.

Purpose of the Study:

  • To introduce a sphere-based registration method to minimize fiducial localization error (FLE) in dental model tracking.
  • To enhance the accuracy of tracking and coregistration for clinical analysis of masticatory functions.

Main Methods:

  • A sphere-based registration method using spheres placed in polygonal concavities of physical and virtual dental models.
  • An optical system tracked active markers on dental casts for experiments.
  • Leave-one-sphere-out method estimated target registration error.

Main Results:

  • The proposed method demonstrated negligible fiducial localization error (FLE).
  • FLE was 5 and 10 times smaller than conventional methods on physical and virtual models, respectively.
  • In vitro accuracy was confirmed by comparing virtual and physical interocclusal impressions.

Conclusions:

  • The sphere-based registration method offers a non-invasive and accurate approach to dental model tracking.
  • The method significantly reduces fiducial localization error compared to conventional techniques.
  • The principles can be applied to track various biomedical and non-biomedical geometries with polygonal concavities.