Abstract

We present an effective method for the accurate three-dimensional (3D) measurement of small industrial parts under a complicated noisy background, based on stereo vision. To effectively extract the nonlinear features of desired curves of the measured parts in the images, a strategy from coarse to fine extraction is employed, based on a virtual motion control system. By using the multiscale decomposition of gray images and virtual beam chains, the nonlinear features can be accurately extracted. By analyzing the generation of geometric errors, the refined feature points of the desired curves are extracted. Then the 3D structure of the measured parts can be accurately reconstructed and measured with least squares errors. Experimental results show that the presented method can accurately measure industrial parts that are represented by various line segments and curves.

© 2010 Optical Society of America

Full Article  |  PDF Article

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Figures (21)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Equations (27)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Metrics

You do not have subscription access to this journal. Article level metrics are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription