Constraining deformable templates for shape recognition

Teresa Aixut; Yuri L. de Meneses; Fabien Bourgeois; Jacques Jacot

doi:10.1117/12.514930

1 May 2003 Constraining deformable templates for shape recognition

Teresa Aixut, Yuri L. de Meneses, Fabien Bourgeois, Jacques Jacot

Proceedings Volume 5132, Sixth International Conference on Quality Control by Artificial Vision; (2003) https://doi.org/10.1117/12.514930
Event: Quality Control by Artificial Vision, 2003, Gatlinburg, TE, United States

Abstract

This paper addresses the problem of robust shape recognition in the presence of shape deformation as well as changes in part position, orientation and scale. Point Distribution Model (PDM) are deformable templates that have interesting features for industrial inspection tasks, since they are built by statistical analysis of a training set and they define a prototype shape as well a set of possible, acceptable deformations. To further improve their classification capabilities, these deformable templates are extended by adding a constraint on the amount of deformation. A constrained optimization procedure is proposed and successfully tested on an industrial inspection task.

Citation Download Citation

Teresa Aixut, Yuri L. de Meneses, Fabien Bourgeois, and Jacques Jacot "Constraining deformable templates for shape recognition", Proc. SPIE 5132, Sixth International Conference on Quality Control by Artificial Vision, (1 May 2003); https://doi.org/10.1117/12.514930

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