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Control of tracking systems by image correlation

Published online by Cambridge University Press:  09 March 2009

Joseph Ciccotelli ,
Michel Dufaut  and
René Husson
Joseph Ciccotelli
Affiliation:
Centre de Recherche en Automatique de Nancy C.N.R.S. UA 821 B.P. 850, 54011 Nancy Cedex, (France)
Michel Dufaut
Affiliation:
Centre de Recherche en Automatique de Nancy C.N.R.S. UA 821 B.P. 850, 54011 Nancy Cedex, (France)
René Husson
Affiliation:
Centre de Recherche en Automatique de Nancy C.N.R.S. UA 821 B.P. 850, 54011 Nancy Cedex, (France)
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Summary

Owing to advances in machine vision, it is now possible to study automatic gripping of moving parts. This complex task requires a precise knowledge of the displacements of objects in a camera field.

In this paper, a method to analyse the motion of parts is presented; it is based on the correlation of numerical images. The treatment of data provided by the image background makes this method quite original.

The utilization of this method, often considered as rather awkward, makes it possible, in this case, to develop a position feedback operation of the robot actuators controlled in an open loop (step by step motors).

Keywords

Dynamic scene analysisBinary correlationTracking feedback systemRobotics
Type
Article
Information
Robotica , Volume 5 , Issue 3 , July 1987 , pp. 201 - 206
Copyright
Copyright © Cambridge University Press 1987

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References

1.Letellier, P., “Le mouvement”, Séminaire Vision Assistée par Ordinateur, Institut National de Recherches en Informatique et Automatique, Rocquencourt, France (06, 1983).Google Scholar
2.Coulon, P.Y., “Association vision-commande en robotique” Thè se 3° cycle, Institut National Polytechnique de Grenoble, France, (11, 1982) Ch, III, pp. 1–20.Google Scholar
3.Squalli, A., “Méthodes de détermination d'un champ de vitesse sur un modèle hydraulique par analyse de séquences d'images” Thè se D.I. Université Paris Sud, France (07, 1983).Google Scholar
4.Chow, W.K. & Aggarwal, J.K., “Computer analysis of planar curvilinear moving images” IEEE Transactions on Computers C.26, 179–185 (02, 1977).Google Scholar
5.Hildreth, E.C., “Computing the velocity field along contours” ACM Interdisciplinary Workshop on Motion, Toronto, Canada26–32 (04, 1983).Google Scholar
6.Yachida, M., “Determining velocity maps by spatio-temporal neighborhoods from image sequences” Computer Graphics and Image Processing 21, 262–279 (1983).Google Scholar
7.Hall, E.L., Davies, D.L. & Casey, M.E., “The selection of critical subsets for signal, image and scene matching” IEEE Transactions on Pattern Analysis and Machine Intelligence 2, No. 4; 313–322 (07, 1980).CrossRefGoogle ScholarPubMed