Features Detection from Industrial Noisy 3D CT Data for Reverse Engineering
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 to CT scanned image in order to segment the data. Next, in order to reduce noises, we exploited nonlocal means method 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, which is for noises removal. In the paper, the result of detecting both features is presented.
KeywordsSharp Edge Reverse Engineering Feature Detection Segmented Surface Data Voxel
Unable to display preview. Download preview PDF.
- 1.Hubeli, A., Gross, M.: Multiresolution Feature Extraction for Unstructured Meshes. In: Proceedings of IEEE Visualization, pp. 287–294 (2001)Google Scholar
- 2.Song, B., Chan, T.: A Fast Algorithm for Level Set Based Optimization. CAM-UCLA 68 (2002)Google Scholar
- 4.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)Google Scholar
- 5.Watanabe, K., Belyaev, A.G.: Detection of Salient Curvature Features on Polygonal Surfaces. Computer Graphics Forum, 385–392 (2001)Google Scholar
- 7.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)Google Scholar
- 10.Morgenthaler, M., Rosenfeld, A.: Multidimensional Edge Detection by Hyper-surface Fitting. IEEE Trans. Pattern Anal. Mach. Intell., 482–486 (1981)Google Scholar
- 11.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)Google Scholar
- 12.Gumhold, S., Wang, X., McLeod, R.: Feature Extraction from Point Clouds. In: Proceedings of 10th International Meshing Roundtable (2001)Google Scholar
- 14.Zucker, S.W., Hummed, R.A.: A Three Dimensional Edge Operator. IEEE Transaction on Pattern Analysis and Machine Intelligence 3 (1981)Google Scholar
- 15.Lorensen, W.E., Cline, H.E.: Marching Cubes: A High Resolution 3D Surface Construction Algorithm. Computer Graphics 21(4) (1987)Google Scholar
- 18.Osher, S., Fedkiw, R.: Level Set Method and Dynamic Implicit Surfaces. Springer (2003)Google Scholar
- 19.Dong, B., Ye, J., Osher, S., Dinov, I.D.: Level Set Based Nonlocal Surface Restoration. Multiscale Modeling and Simulation, 589–598 (2008)Google Scholar