You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.
25 July 2002Multivariate regression as a method for FLIR target detection
I promote using an alternative philosophy for the design of infrared-target-detection algorithms. This philosophy focuses on finding first and eliminating natural clutter from a scene, followed by finding and preserving candidate targets in that scene. The reverse approach is the most common adopted one in the infrared ATR (automatic target recognition) community. This alternative is appealing because it should significantly reduce the amount of out-of-context information to be processed by a classifier. I show how to apply sensor domain knowledge, common sense, and multivariate regression to the problem of infrared target detection. A proof-of-principle experiment and its results are discussed.
The alert did not successfully save. Please try again later.
Dalton S. Rosario, "Multivariate regression as a method for FLIR target detection," Proc. SPIE 4726, Automatic Target Recognition XII, (25 July 2002); https://doi.org/10.1117/12.477016