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Updated: May 17, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Improving class separability using extended pixel planes: a comparative study.

Nikita V Orlov1, D Mark Eckley, Lior Shamir

  • 1National Institute on Aging /National Institutes of Health 251 Bayview Blvd, Bayview Research Center Bld, Suite 100, Baltimore, MD 21224, U.S.

Machine Vision and Applications
|October 18, 2012
PubMed
Summary
This summary is machine-generated.

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Extended representations of pixel planes (EPP) enhance class separability in feature spaces. Transform-based EPP notably improve separation, especially for underdeveloped feature libraries, aiding new applications.

Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Feature engineering is crucial for pattern recognition.
  • Developing effective feature representations is challenging, especially for novel applications.

Purpose of the Study:

  • To investigate class separability using extended representations of pixel planes (EPP).
  • To evaluate the impact of different EPP generation methods (scale pyramid, subband pyramid, image transforms) on feature space separability.
  • To assess the effectiveness of EPP with suboptimal feature libraries.

Main Methods:

  • Generated EPP using scale pyramid, subband pyramid, and various image transforms (Chebyshev, Fourier, wavelets, gradient, Laplacian).
  • Explored combinations of these image transforms.

Related Experiment Videos

Last Updated: May 17, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

  • Evaluated EPP performance with feature libraries containing only textural or Haralick features.

    • Main Results:

    • All three EPP types (scale pyramid, subband pyramid, image transforms) demonstrated improved class separation.
    • Transform-based EPP significantly enhanced separability, outperforming scale and subband pyramids, particularly with suboptimal feature libraries.
    • EPP proved highly beneficial for feature libraries lacking optimal features.

    Conclusions:

    • Extended representations of pixel planes (EPP) are effective in improving class separability.
    • Image transform-based EPP offer superior performance, especially when optimal features are not yet established.
    • EPP provides a valuable approach for enhancing feature representation in machine learning and computer vision tasks.