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Hierarchical morphological segmentation for image sequence coding.

P Salembier1, M Pardas

  • 1Dept. de Teoria del Senyal i Comunicacions, Univ. Politecnica de Catalunya, Barcelona.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1994
PubMed
Summary
This summary is machine-generated.

This study introduces a hierarchical morphological segmentation algorithm for image sequence coding. This 3-D approach enhances segmentation stability and region correspondence for efficient video compression.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Video Coding

Background:

  • Mathematical morphology offers efficient geometric feature analysis for image segmentation.
  • Hierarchical segmentation refines initial coarse segmentations with local details.
  • 3-D approaches in image sequence analysis ensure temporal stability and solve region correspondence.

Purpose of the Study:

  • To present a hierarchical morphological segmentation algorithm for image sequence coding.
  • To develop a 3-D segmentation method for temporally stable video analysis.
  • To integrate segmentation and coding processes for improved efficiency.

Main Methods:

  • A top-down hierarchical approach segmenting 3-D regions directly.
  • Utilizing morphological filters based on partial reconstruction for simplification.
  • Employing constrained flat region labeling and morphological contrast extraction for marker extraction.
  • Applying a modified watershed algorithm for precise contour detection.
  • Incorporating quality estimation focusing on coding residue for iterative refinement.

Main Results:

  • Demonstrated a stable, time-consistent segmentation of 3-D regions in image sequences.
  • Successfully integrated texture and contour coding schemes within the segmentation process.
  • Showcased improved segmentation and coding quality through iterative refinement using coding residue.

Conclusions:

  • The proposed hierarchical morphological segmentation algorithm is effective for image sequence coding.
  • The 3-D approach ensures temporal stability and simplifies region correspondence.
  • Integrating coding residue into subsequent stages enhances overall segmentation and coding performance.