Abstract

We present a method based on Bayesian estimation with prior Markov random field models for segmentation of range images of polyhedral objects. This method includes new ways to determine the confidence associated with the information given for every pixel in the image as well as an improved method for localization of the boundaries between regions. The performance of the method compares favorably with other state-of-the-art procedures when evaluated using a standard benchmark.

© 2008 Optical Society of America

Full Article  |  PDF Article
Related Articles
Building extraction from stereoscopic aerial images

Hélène Oriot and Alain Michel
Appl. Opt. 43(2) 218-226 (2004)

Robust segmentation of intraretinal layers in the normal human fovea using a novel statistical model based on texture and shape analysis

Vedran Kajić, Boris Považay, Boris Hermann, Bernd Hofer, David Marshall, Paul L. Rosin, and Wolfgang Drexler
Opt. Express 18(14) 14730-14744 (2010)

3D photon counting integral imaging with unknown sensor positions

Xiao Xiao and Bahram Javidi
J. Opt. Soc. Am. A 29(5) 767-771 (2012)

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 (12)

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

Tables (3)

You do not have subscription access to this journal. Article tables 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 (22)

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