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Updated: Jul 10, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

A theory of automatic parameter selection for feature extraction with application to feature-based multisensor image

Stephen P DelMarco1, Victor Tom, Helen F Webb

  • 1BAE Systems, Burlington, MA 01803, USA. stephen.delmarco@baesystems.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|November 10, 2007
PubMed
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This study enhances automated edge-detection parameter selection for broader applications, including multisensor image registration. A new sensitivity measure guides optimal parameter space sampling for improved feature detection.

Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Geometry

Background:

  • Automated parameter selection algorithms are crucial for image analysis.
  • Existing methods, like Yitzhaky and Peli's, are limited in scope.
  • Generalizing these algorithms is essential for advanced applications such as multisensor image registration.

Purpose of the Study:

  • To generalize the automated edge-detection parameter selection algorithm.
  • To enable the use of more general image features and parameter spaces.
  • To investigate parameter space sampling density selection for improved feature-based image registration.

Main Methods:

  • Generalization of the parameter selection approach to multidimensional, continuous, or discrete spaces.
  • Development of a real-valued sensitivity measure to assess sampling density effects.

Related Experiment Videos

Last Updated: Jul 10, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

  • Analysis of the sensitivity measure's convergence properties and derivation of closed-form solutions.

    • Main Results:

    • The generalized algorithm accommodates arbitrary parameter and feature spaces.
    • A novel sensitivity measure quantifies the impact of sampling density on feature variability.
    • Convergence properties of the sensitivity measure were analyzed, with closed-form solutions for specific cases.

    Conclusions:

    • The developed sensitivity measure aids in selecting appropriate parameter values and sampling densities.
    • The generalized approach enhances automated parameter selection for feature-based multisensor image registration.
    • Numerical results demonstrate the practical utility of the sensitivity measure.