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Related Experiment Video

Updated: Jun 12, 2026

Automatic Identification of Dendritic Branches and their Orientation
06:08

Automatic Identification of Dendritic Branches and their Orientation

Published on: September 17, 2021

Fast and memory efficient 2-D connected components using linked lists of line segments.

Johan De Bock1, Wilfried Philips

  • 1Department of Telecommunications and Information Processing, Ghent University, Belgium. johan.debock@telin.ugent.be

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|June 17, 2010
PubMed
Summary

This study introduces a novel, memory-efficient algorithm for identifying connected components in binary images. It utilizes a linked list of line segments, significantly outperforming traditional methods in speed and resource usage.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Algorithm Design

Background:

  • Conventional connected components algorithms rely on region label images, which can be memory-intensive.
  • Existing algorithms often require multiple raster scans or complex image access patterns.

Purpose of the Study:

  • To develop a more efficient algorithm for finding connected components in binary images.
  • To reduce memory usage and improve processing speed compared to traditional methods.

Main Methods:

  • Replaced the traditional region label image with a singly-linked list of line segments for each region.
  • Developed a single-pass algorithm with regular image access, requiring only the three most recent image lines.

Main Results:

  • The proposed algorithm is considerably faster than the fastest conventional algorithms.
  • The novel region coding data structure uses significantly less memory in typical cases.
  • Processing time remains linear with the number of pixels, even in worst-case scenarios.

Conclusions:

  • The new algorithm offers substantial improvements in both speed and memory efficiency for connected components analysis.
  • This approach provides a practical and effective alternative for binary image processing tasks.