Abstract

We propose and assess new algorithms for detecting and locating an object in multichannel images. These algorithms are optimal for additive Gaussian noise and maximize the likelihood of the observed images. We consider two cases, in which the illumination of the target and the variance of the noise in each channel are either known or unknown. We show that in the latter case the algorithm provides accurate estimates of variance and luminance. These algorithms can be viewed as postprocessed versions of the correlation of a reference with the scene image in each channel.

© 1997 Optical Society of America

Full Article  |  PDF Article
OSA Recommended Articles
Optimal multichannel estimation of the location of a target with nonoverlapping noise

J. Campos, S. Lhostis, and M. Guillaume
J. Opt. Soc. Am. A 17(12) 2140-2147 (2000)

Optimal processors for images with an arbitrary number of gray levels

Henrik Sjöberg and Bertrand Noharet
J. Opt. Soc. Am. A 17(11) 1982-1992 (2000)

Optimal snake-based segmentation of a random luminance target on a spatially disjoint background

Olivier Germain and Philippe Réfrégier
Opt. Lett. 21(22) 1845-1847 (1996)

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 (2)

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 (8)

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