Abstract
A useful filter for pattern recognition must strike a compromise between
the conflicting requirements of in-class distortion tolerance and out-of-class
discrimination. Such a filter will be bandpass in nature, the high-frequency
response being attenuated to provide less sensitivity to in-class variations,
while the low frequencies must be removed, since they compromise the
discrimination ability of the filter. A convenient bandpass is the difference
of Gaussian (DOG) function, which provides a close approximation to the
Laplacian of Gaussian. We describe the effect of a preprocessing operation
applied to a DOG filtered image. This operation is shown to result in greater
tolerance to in-class variation while maintaining an excellent discrimination
ability. Additionally, the introduction of nonlinearity is shown to provide
greater robustness in the filter response to noise and background clutter in
the input scene.
© 1997 Optical Society of America
Full Article |
PDF Article
More Like This
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
Tables (7)
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 (15)
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