Special Section on Quality Control by Artificial Vision

Automated delineation of regions of interest through adaptive segmentation and interpretation of images in view of dimensional evaluation of manufactured parts

[+] Author Affiliations
Alex Lallement, Redouane Khemmar, Ernest Hirsch

Universite´ Louis Pasteur, E´cole Nationale Supe´rieure de Physique de Strasbourg, Laboratoire des Sciences de l’Image, de l’Informatique et de la Te´le´de´tection, (UMR ULP-CNRS 7005), Parc d’innovation, Boulevard Se´bastien Brant, BP 10413, F-67412?Illkirch-Cedex, France E-mail: alex.lallement@ensps.u-strasbg.fr

J. Electron. Imaging. 13(3), 462-473 (Jul 01, 2004). doi:10.1117/1.1762517
History: Received Jul. 25, 2003; Accepted Feb. 27, 2004; Online July 29, 2004
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Industrial applications of computer vision, such as dimensional inspection, require, among other things, making automated procedures available for the analysis of gray-level image to derive application-dependent 3-D descriptions of their contents. In view of the quantitative 3-D reconstruction of an imaged object and to fully automate the evaluation process, we describe two segmentation and interpretation methods for the automated delineation of regions of interest belonging to the object and associated with free-form surfaces. The selection of the procedure to apply, either a mean shift based or level set based analysis, depends essentially on available a priori information relative to the localization and the shape of the object in the scene. The two approaches are part of a 3-D vision-based on-line inspection system. Results on images of manufactured parts acquired under realistic conditions illustrate the use of these two approaches. © 2004 SPIE and IS&T.

© 2004 SPIE and IS&T

Citation

Alex Lallement ; Redouane Khemmar and Ernest Hirsch
"Automated delineation of regions of interest through adaptive segmentation and interpretation of images in view of dimensional evaluation of manufactured parts", J. Electron. Imaging. 13(3), 462-473 (Jul 01, 2004). ; http://dx.doi.org/10.1117/1.1762517


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