Features Detection from Industrial Noisy 3D CT Data for Reverse Engineering

  • Thi-Chau Ma
  • Chang-soo Park
  • Kittichai Suthunyatanakit
  • Min-jae Oh
  • Tae-wan Kim
  • Myung-joo Kang
Part of the Studies in Computational Intelligence book series (SCI, volume 413)


To detect features are significantly important for reconstructing a model in reverse engineering. In general, it is too difficult to find the features from the original industrial 3D CT data because the data have many noises. So it is necessary to reduce the noises for detecting features. This paper proposes a new method for detecting corner features and edge features from noisy 3D CT scanned data. First, we applied the level set method[18] to CT scanned image in order to segment the data. Next, in order to reduce noises, we exploited nonlocal means method[19] to the segmented surface. This helps to detect the edges and corners more accurately. Finally, corners and sharp edges are detected and extracted from the boundary of the shape. The corners are detected based on Sobel-like mask convolution processing with a marching cube. The sharp edges are detected based on Canny-like mask convolution with SUSAN method[13], which is for noises removal. In the paper, the result of detecting both features is presented.


Sharp Edge Reverse Engineering Feature Detection Segmented Surface Data Voxel 
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 GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Thi-Chau Ma
    • 1
  • Chang-soo Park
    • 2
  • Kittichai Suthunyatanakit
    • 1
  • Min-jae Oh
    • 1
  • Tae-wan Kim
    • 1
  • Myung-joo Kang
    • 2
  1. 1.Department of Naval Architecture and Ocean EngineeringSeoul National UniversitySeoulKorea
  2. 2.Department of Mathematical SciencesSeoul National UniversitySeoulKorea

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