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Range-data-based object surface segmentation via edges and critical points.

D Zhao1, X Zhang

  • 1Dept. of Electr. and Comput. Eng., Michigan Univ., Dearborn, MI.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1997
PubMed
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This study introduces a new method for segmenting range images using edge and region data. The technique efficiently describes object surface structures for 3-D modeling and recognition.

Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Geometry

Background:

  • Range image segmentation is crucial for 3-D object reconstruction.
  • Existing methods may struggle with complex surface structures.
  • Accurate segmentation is vital for subsequent analysis and modeling.

Purpose of the Study:

  • To present a novel range image segmentation method.
  • To integrate edge and region information for improved segmentation.
  • To enable the creation of surface structure graphs for 3-D object representation.

Main Methods:

  • The algorithm integrates edge and region information.
  • Key steps include edge/critical point detection, triangulation, and region growing.
  • A three-dimensional (3-D) surface structure graph (SSG) is generated.

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Main Results:

  • The method demonstrates efficiency in segmenting range images.
  • It is particularly effective for images containing polyhedral objects.
  • The segmentation yields a descriptive SSG of the object's surface.

Conclusions:

  • The proposed method offers an efficient approach to range image segmentation.
  • The generated SSG serves as a valuable surface model description.
  • This facilitates applications in computer-aided-design (CAD)-based vision and object recognition.