Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Hubeli, A., Gross, M.: Multiresolution Feature Extraction for Unstructured Meshes. In: Proceedings of IEEE Visualization, pp. 287–294 (2001)
Song, B., Chan, T.: A Fast Algorithm for Level Set Based Optimization. CAM-UCLA 68 (2002)
Canny, J.: A Computational Approach to Edge Detection. TPAMI 8(6), 679–698 (1986)
Weber, C., Hahmann, S., Hagen, H.: Methods for Feature Detection in Point Clouds. In: Visualization of Large and Unstructured Data Sets - Applications in Geospatial Planning, Modeling and Engineering (IRTG 1131 Workshop) (2010)
Watanabe, K., Belyaev, A.G.: Detection of Salient Curvature Features on Polygonal Surfaces. Computer Graphics Forum, 385–392 (2001)
Demarsin, K., Vanderstraeten, D., Volodine, T., Roose, D.: Detection of Closed Sharp Edges in Point Clouds using Normal Estimation and Graph Theory. Computer-Aided Design 39(4), 276–283 (2007)
Hildebrand, K., Polthier, K., Wardetzky, M.: Smooth Feature Lines on Surface Meshes. In: Proceedings of 3rd Eurographics Symposium on Geometry Processing, pp. 85–90 (2005)
Pauly, M., Keiser, R., Gross, M.: Multi-scale Feature Extraction on Point Sampled Surfaces. Computer Graphics Forum 22, 281–289 (2003)
Monga, O., Deriche, R., Rocchisani, J.: 3D Edge Detection using Recursive Filtering: Application to Scanner images. CVGIP - Image Underst. 53(1), 76–87 (1991)
Morgenthaler, M., Rosenfeld, A.: Multidimensional Edge Detection by Hyper-surface Fitting. IEEE Trans. Pattern Anal. Mach. Intell., 482–486 (1981)
Monga, O., Deriche, R., Malandain, G., Cocquerez, J.P.: Recursive Filtering and Edge Closing: Two Primary Tools for 3D Edge Detection. In: Proceedings of the First European Conference on Computer Vision (1990)
Gumhold, S., Wang, X., McLeod, R.: Feature Extraction from Point Clouds. In: Proceedings of 10th International Meshing Roundtable (2001)
Smith, S.M., Brady, J.M.: SUSAN - A New Approach to Low Level Image Processing. Int. J. Computer Vision 23(1), 45–78 (1997)
Zucker, S.W., Hummed, R.A.: A Three Dimensional Edge Operator. IEEE Transaction on Pattern Analysis and Machine Intelligence 3 (1981)
Lorensen, W.E., Cline, H.E.: Marching Cubes: A High Resolution 3D Surface Construction Algorithm. Computer Graphics 21(4) (1987)
Mumford, D., Shah, J.: Optimal Approximation by Piecewise Smooth Functions and Associated Variational Problems. Commun. Pure. Appl. Math 42, 577–685 (1989)
Chan, T., Vese, L.: Active Contours without Edges. IEEE Transactions on Image Processing 10(2), 266–277 (2001)
Osher, S., Fedkiw, R.: Level Set Method and Dynamic Implicit Surfaces. Springer (2003)
Dong, B., Ye, J., Osher, S., Dinov, I.D.: Level Set Based Nonlocal Surface Restoration. Multiscale Modeling and Simulation, 589–598 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag GmbH Berlin Heidelberg
About this chapter
Cite this chapter
Ma, TC., Park, Cs., Suthunyatanakit, K., Oh, Mj., Kim, Tw., Kang, Mj. (2012). Features Detection from Industrial Noisy 3D CT Data for Reverse Engineering. In: Lee, R. (eds) Software and Network Engineering. Studies in Computational Intelligence, vol 413. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28670-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-28670-4_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28669-8
Online ISBN: 978-3-642-28670-4
eBook Packages: EngineeringEngineering (R0)