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Towards improving edge quality using combinatorial optimization and a novel skeletonize algorithm.

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Summary
This summary is machine-generated.

This study introduces a novel skeletonization algorithm and parameter search to refine object boundaries into precise, one-pixel wide edges. This computer vision pipeline significantly improves edge detection accuracy, aiding various image analysis tasks.

Keywords:
Computational optimizationEdge detectionPost-processingSkeletonize algorithm

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Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Imaging

Background:

  • Object detection and image segmentation are crucial for many scientific and technical fields.
  • Accurate computer vision methods are essential for image-based tasks.
  • Existing edge detection algorithms often struggle to produce thin, topology-preserving boundaries.

Purpose of the Study:

  • To develop a computational pipeline for extracting precise, one-pixel wide edges from predicted object boundaries.
  • To improve the accuracy and reliability of edge representation in image analysis.
  • To provide guidance on parameter tuning and algorithm selection for post-processing object boundaries.

Main Methods:

  • A novel skeletonization algorithm is introduced to transform object boundaries into thin edges.
  • A discrete parameter search optimizes post-processing pipeline parameters.
  • Evaluation is performed on three diverse datasets: kidney boundaries, NYU-Depth V2, and BSDS 500.

Main Results:

  • The proposed skeletonization algorithm and post-processing pipeline demonstrate significant improvements in edge accuracy.
  • The Signed Distance Error (SDE) metric for edge detection is improved up to 2.3 times compared to existing methods.
  • The pipeline's validity is confirmed through comparison with classical topological skeletons and integration of prior post-processing techniques.

Conclusions:

  • The developed computational pipeline effectively refines object boundaries into accurate, topology-preserving edges.
  • The study offers valuable insights for selecting and tuning algorithms in image post-processing.
  • This work advances the precision of edge extraction in computer vision applications.