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Smoothing random noise from human head scan data.

H Fang1, J H Nurre

  • 1Sch. of Electr. Eng. & Comput. Sci., Ohio Univ., Athens, OH, USA.

IEEE Transactions on Medical Imaging
|January 1, 1996
PubMed
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Noise in laser-scanned head data can be reduced using a Gaussian filter. A cross-validation method effectively determines the optimal filter size, preserving data integrity for accurate 3D head models.

Area of Science:

  • Computer Vision
  • Medical Imaging
  • 3D Scanning

Background:

  • Laser scanning for human head data acquisition is prone to noise from system errors and surface irregularities.
  • Effective noise reduction is crucial for accurate 3D head modeling and analysis.

Purpose of the Study:

  • To identify an optimal filtering technique for smoothing noisy human head range data.
  • To develop a robust method for determining the appropriate parameters for noise reduction filters.

Main Methods:

  • Utilized a Gaussian filter, known for its data integrity preservation properties.
  • Derived and applied a cross-validation method based on regularization theory to estimate optimal filter size.
  • Extended generalized cross-validation to the two-dimensional case for head scan data.

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Main Results:

  • The Gaussian filter effectively reduces noise without introducing significant artifacts.
  • The proposed cross-validation technique accurately determines the optimal filter size for smoothing.
  • Experimental results demonstrate the method's effectiveness and robustness in practical applications.

Conclusions:

  • The Gaussian filter, with parameters optimized by cross-validation, is a suitable method for noise reduction in human head range data.
  • This approach ensures high-fidelity 3D head models by minimizing noise while preserving essential data features.