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Related Experiment Video

Updated: Jun 27, 2026

Precision Measurements and Parametric Models of Vertebral Endplates
10:35

Precision Measurements and Parametric Models of Vertebral Endplates

Published on: September 17, 2019

Curve parametrization by moments.

Irina Popovici1, William Douglas Withers

  • 1United States Naval Academy, Annapolis, MD 21402-5002, USA. popovici@usna.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|November 26, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a novel moment-based method for image analysis, efficiently describing conic sections (quadratic curves) without large accumulator arrays. The technique offers robust and efficient parameter determination, even with noisy image data.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Geometry

Background:

  • Traditional methods like the Hough transform for curve detection can be computationally intensive and sensitive to noise.
  • Deriving curve parameters directly from image features is crucial for various computer vision applications.

Purpose of the Study:

  • To develop a computationally efficient and robust method for parametric description of conic sections in images.
  • To overcome limitations of accumulator-based methods by utilizing image moments.

Main Methods:

  • A novel approach using image moments with specially-constructed kernel functions to derive conic section parameters.
  • Calculation of parameters involves a fixed number of operations per pixel, avoiding large accumulator arrays.

Main Results:

  • The moment-based method requires minimal computational resources (5 multiplications, 6 additions per pixel).
  • The approach demonstrates robustness against high-frequency noise, texture, and minor edge irregularities.
  • Successfully generalizes to more complex curves and higher-dimensional surfaces.

Conclusions:

  • The proposed moment-based method offers a computationally efficient and robust alternative for conic section description in images.
  • This technique has potential applications in image analysis, pattern recognition, and 3D reconstruction.