Segmentation of dense 3D data using a neural network approach

George K. Knopf

doi:10.1117/12.222690

3 October 1995 Segmentation of dense 3D data using a neural network approach

George K. Knopf

Proceedings Volume 2588, Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling; (1995) https://doi.org/10.1117/12.222690
Event: Photonics East '95, 1995, Philadelphia, PA, United States

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.

Citation Download Citation

George K. Knopf "Segmentation of dense 3D data using a neural network approach", Proc. SPIE 2588, Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling, (3 October 1995); https://doi.org/10.1117/12.222690

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available