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.
Hundreds of simple target-detection algorithms were tested on mid- and long-wave forward-looking infrared images. Each algorithm is briefly described. Indications are given as to which performed well. Most of these simple algorithms are loosely derived from standard tests of the difference of two populations. For target detection, these are populations of pixel grayscale values or features derived from them. The statistical tests are implemented in the form of sliding triple window filters. Several more elaborate algorithms are also described with their relative performances noted. They utilize neural networks, deformable templates, and adaptive filtering. Algorithm design issues are broadened to cover system design issues and concepts of operation. Since target detection is such a fundamental problem, it is often used as a test case for developing technology. New technology leads to innovative approaches for attacking the problem. Eight inventive paradigms, each with deep philosophical underpinnings, are described in relation to their effect on target detector design.