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Image segmentation with directed trees.

P M Narendra1, M Goldberg

  • 1Systems and Research Center, Honeywell, Inc., Minneapolis, MN 55413.

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

This study introduces a novel algorithm for image segmentation using directed trees. The method effectively detects homogeneous regions and handles varying edge types without thresholding, proving efficient even with noisy data.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Image segmentation is crucial for analyzing visual data.
  • Existing methods often struggle with noise and varying edge characteristics.
  • A robust and efficient segmentation algorithm is needed.

Purpose of the Study:

  • To present a simple and efficient algorithm for detecting and labeling homogeneous image areas.
  • To utilize directed trees for region labeling and image segmentation.
  • To demonstrate the algorithm's effectiveness with varying edge types and in the presence of noise.

Main Methods:

  • Constructing directed trees with image points as nodes, guided by computed edge values.
  • Employing a valley sealing property for precise boundary placement.
  • Segmenting images into disjoint regions based on the tree structure.

Main Results:

  • The algorithm successfully segments images into homogeneous regions.
  • Boundaries accurately pass through the center of edges due to the valley sealing property.
  • The method performs well with thick, wide edges and is robust to noise.

Conclusions:

  • The proposed directed tree-based algorithm is simple, efficient, and effective for image segmentation.
  • It overcomes limitations of thresholding-based methods, particularly with complex edges.
  • The algorithm shows promise for applications like segmenting multispectral scenes.