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Unsupervised contour closure algorithm for range image edge-based segmentation.

Angel Domingo Sappa1

  • 1Computer Vision Center, 08193 Bellaterra, Barcelona, Spain. angel.sappa@cvc.uab.es

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 17, 2006
PubMed
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This study introduces an efficient method for extracting closed contours from range image edge points using graph partitioning and minimum spanning trees. The technique effectively closes open contours and removes noise for improved 3D data analysis.

Area of Science:

  • Computer Vision
  • 3D Data Processing
  • Computational Geometry

Background:

  • Extracting closed contours from range images is crucial for 3D object recognition and scene understanding.
  • Existing methods for contour extraction from edge points can be sensitive to noise and may struggle with contour closure.

Purpose of the Study:

  • To present an efficient and robust technique for extracting closed contours from edge points in range images.
  • To reduce the contour closure problem to a minimum spanning tree partitioning problem combined with cost function minimization.

Main Methods:

  • Generate a partially connected graph from input edge points.
  • Compute the minimum spanning tree (MST) of the generated graph.
  • Apply a postprocessing step to remove noisy links and close open contours, forming a single path.

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Main Results:

  • The proposed method successfully extracts closed contours from range image edge points.
  • Experimental results demonstrate the efficiency and effectiveness of the technique on synthetic and real range data.
  • Comparisons with a previous technique show improved performance in contour extraction.

Conclusions:

  • The novel approach effectively addresses the contour closure problem by leveraging graph theory and MST algorithms.
  • This technique offers an efficient solution for deriving meaningful boundary representations from noisy edge data in range images.
  • The method has potential applications in various fields requiring precise 3D shape analysis.