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A Segmentation Methodology for Real 3D Images

  • M. Razaz
  • D. M. P. Hagyard
  • R. A. Lee
Conference paper

Abstract

Segmentation is the process of dividing an image into segments that have similar attributes. There are a variety of traditional segmentation technique in the literature, mainly for 2D image processing applications. 3D segmentation is a much more complex problem. This is even harder for real 3D data sets where the images are degraded by blur and noise, the background is nonuniform and objects do not possess clear cut boundaries. These traditional techniques are usually unsuitable for segmentation of real 3D images. In this paper we present a novel and effective data driven segmentation framework based on a combination of nonlinear restoration and watershedding. The framework is presented and discussed as are experimental results showing its effectiveness in accurately segmenting real 3D images.

Keywords

Catchment Region Watershed Line Morphological Segmentation Point Spread Func Segmentation Methodology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London Limited 1998

Authors and Affiliations

  • M. Razaz
    • 1
  • D. M. P. Hagyard
    • 1
  • R. A. Lee
    • 1
  1. 1.School of Information SystemsUniversity of East AngliaNorwichEngland

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