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Block-Based Connected-Component Labeling Algorithm Using Binary Decision Trees.

Wan-Yu Chang1, Chung-Cheng Chiu2, Jia-Horng Yang3

  • 1Department of Electrical and Electronic Engineering, Chung Cheng Institute of Technology, National Defense University, Taoyuan County 33551, Taiwan. wychang@gmail.com.

Sensors (Basel, Switzerland)
|September 23, 2015
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Summary
This summary is machine-generated.

This study introduces a novel block-based labeling algorithm designed to minimize memory access. The new approach significantly speeds up image labeling processes compared to existing methods.

Keywords:
connected componentsdecision treelabeling algorithmscan mask

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Area of Science:

  • Computer Vision
  • Image Processing
  • Algorithms

Background:

  • Labeling algorithms are crucial for image analysis.
  • Efficiency is often limited by memory access operations.
  • Minimizing neighborhood operations is key to faster labeling.

Purpose of the Study:

  • To propose a fast labeling algorithm.
  • To reduce memory access points and neighborhood operations.
  • To enhance the efficiency of image labeling.

Main Methods:

  • Utilizes a block-based view and raster scan.
  • Employs a block-based scan mask to select pixels.
  • Integrates block-connected relationships using binary decision trees.
  • Reduces unnecessary memory access and simplifies pixel locations.

Main Results:

  • The algorithm significantly reduces memory access.
  • Binary decision trees require fewer leaf nodes and depth levels.
  • Demonstrated faster labeling performance on high-resolution and foreground images.
  • Experimental results show superiority over other methods on synthetic and real datasets.

Conclusions:

  • The proposed block-based labeling algorithm is highly efficient.
  • It offers a significant speed improvement for image labeling tasks.
  • This method is effective for processing high-resolution and complex image datasets.