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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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A locally adaptive peano scanning algorithm.

J Quinqueton1, M Berthod

  • 1INRIA, Rocquencourt, France.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a Peano scanning algorithm, creating a reciprocal mapping from n-dimensional space to one dimension. This technique transforms point sets into a 1D image, enabling new applications.

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Area of Science:

  • Computational Geometry
  • Data Visualization
  • Applied Mathematics

Background:

  • The Peano curve is a well-known space-filling curve.
  • Mapping high-dimensional data to lower dimensions is a significant challenge.

Purpose of the Study:

  • To develop an algorithm for Peano scanning, the reciprocal mapping of the Peano curve.
  • To apply this scanning technique for one-dimensional representation of point sets in n-dimensional space.

Main Methods:

  • Algorithm development for reciprocal Peano mapping.
  • Application of the algorithm to point sets in [0, 1]n.
  • Generation of one-dimensional images from high-dimensional data.

Main Results:

  • Successful construction of the Peano scanning algorithm.
  • Demonstration of the technique's ability to create a 1D image of n-dimensional point data.
  • Presentation of developed applications utilizing this method.

Conclusions:

  • The Peano scanning algorithm provides an effective method for dimensionality reduction.
  • The technique offers a novel approach for visualizing and analyzing high-dimensional data.
  • The presented applications highlight the practical utility of Peano scanning.