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A scale-based connected coherence tree algorithm for image segmentation.

Jundi Ding1, Runing Ma, Songcan Chen

  • 1Department of Computer Science and Engineering, Nanijing University of Aeronautics and Astronautics, Nanjing, China.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 14, 2008
PubMed
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This study introduces the connected coherence tree algorithm (CCTA) for image segmentation without prior knowledge. CCTA effectively identifies semantically coherent regions, improving object segmentation and figure-ground separation even in challenging image conditions.

Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Image segmentation is crucial for understanding visual data.
  • Existing methods often require prior knowledge or struggle with complex image conditions.
  • Developing algorithms for robust semantic segmentation is an ongoing challenge.

Purpose of the Study:

  • To present a novel algorithm, the connected coherence tree algorithm (CCTA), for image segmentation.
  • To achieve segmentation based on semantic coherence without requiring prior knowledge.
  • To enhance the accuracy of object segmentation and figure-ground separation.

Main Methods:

  • The connected coherence tree algorithm (CCTA) utilizes an epsilon-neighbor coherence segmentation criterion.
  • Adaptive spatial and intensity-difference scales are employed to identify coherent neighboring pixels.

Related Experiment Videos

  • Coherent pixels are grouped into coherence classes (CCs), represented by connected coherence trees (CCTs).

    • Main Results:

    • CCTA successfully segments images by grouping pixels into coherence classes, theoretically ensuring separability.
    • The algorithm demonstrates effectiveness in semantic object segmentation and figure-ground separation.
    • Objective and subjective evaluations confirm CCTA's performance across diverse and challenging image types.

    Conclusions:

    • CCTA offers an interpretable, efficient, and effective approach to image segmentation.
    • The algorithm performs well on images with complex backgrounds, tiny objects, noise, and poor illumination.
    • CCTA provides a robust solution for semantic segmentation tasks in various real-world scenarios.