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Automatic feature identification in 3-D measuring data

Presentation held at the 8th Int. Symp. on Measurement and Quality Control in Production (ISMQC). Erlangen, Germany, Oct. 12-15, 2004
 
: Ahn, S.J.; Effenberger, I.; Bolboaca, L.

:
Fulltext urn:nbn:de:0011-n-264435 (245 KByte PDF)
MD5 Fingerprint: b81a57f418b42ee3e3b813fa8f9d8e7d
Created on: 08.02.2005


2004, 9 pp.
International Symposium on Measurement and Quality Control in Production <8, 2004, Erlangen>
English
Presentation, Electronic Publication
Fraunhofer IPA ()
3D-Messen; Punktwolke; Objekterkennung; Software

Abstract
The automatic feature identification in 3-D measuring data is of great interest in many application fields e.g. metrology, computer vision or reverse engineering. In this paper we present a software tool for the fully automatic object detection and parameter estimation in unordered incomplete and even noisy point clouds with a large number of points. The software consists of three interactive modules each for model selection, point segmentation and model fitting, in which our newly developed algorithms for orthogonal distance fitting (ODF) play an important role. The ODF algorithms estimate the model parameters by minimizing the square sum of the shortest distances between the model featrue and the measurement points. The local quadric surface fitted through ODF to a randomly touched small initial patch and the point cloud provides the necessary initial information for the overall procedures of model selection, point segmentation and model fitting. The performance of the presented software tool will be demonstrated on different point clouds.

: http://publica.fraunhofer.de/documents/N-26443.html