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Proceedings Paper

Segmentation of dense 3D data using a neural network approach
Author(s): George K. Knopf
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Paper Abstract

Reverse engineering is the process of generating accurate 3D CAD models of manufactured parts from measured coordinate data. The 3D coordinate data can be acquired from non- contact laser scanning machines or contact coordinate-measuring machines. Prior to creating the CAD model, it is necessary to segment the dense data into regions that are free of any sharp changes in the surface shapes. These segmented regions are then fitted with parametric surface patches for an economized CAD representation. In this paper, a hybrid basis function neural network is proposed for segmenting dense depth data. The first three layers of the network perform the coarse segmentation task by clustering surface features and classifying them as one of eight primitive surface types. The features correspond to the mean curvature (H) and Gaussian curvature (K) of the measured 3D surface. Each surface type image is further partitioned into isolated regions by a series of competitive feedback networks that perform opening and closing morphological operations. Once segmented, each region is parameterized and the associated depth data is approximated by a Bezier surface patch. The corresponding control points are used to reconstruct the parametric surface patch in a typical CAD system.

Paper Details

Date Published: 3 October 1995
PDF: 10 pages
Proc. SPIE 2588, Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling, (3 October 1995); doi: 10.1117/12.222690
Show Author Affiliations
George K. Knopf, Univ. of Western Ontario (Canada)


Published in SPIE Proceedings Vol. 2588:
Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling
David P. Casasent, Editor(s)

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