Abstract

We propose a geometric matching technique in which line segments and elliptical arcs are used as edge features. The use of these higher-order features renders feature representation efficient. We derive distance measures to evaluate the similarity between the features of the model and those of the image. The model transformation parameters are found by searching a 3-D transformation space using cell-decomposition. The performance of the proposed method is quite good when tested on a variety of images.

© 2010 Optical Society of America

Full Article  |  PDF Article
Related Articles
Detection and segmentation of man-made objects in outdoor scenes: concrete bridges

D. C. Baker, S. S. Hwang, and J. K. Aggarwal
J. Opt. Soc. Am. A 6(6) 938-950 (1989)

Optical associative processor for general linear transformations

Raghuram Krishnapuram and David Casasent
Appl. Opt. 26(17) 3641-3648 (1987)

Rotating-kernel min–max algorithms for straight-line feature enhancement

Yim-Kul Lee and William T. Rhodes
Appl. Opt. 34(2) 290-298 (1995)

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 (9)

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 (12)

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