Abstract
A model for analyzing issues involving monospectral target recognition is presented. These issues include modeling target detection, recognition and identification thresholds, and predicting the functional parametric dependencies of the results of observation experiments with human observers. The model makes extensive use of concepts used in information theory. An image of a scene is treated as a sample of an entire set of images of that scene. A difference measure, called the informational difference (InDif) between two image sets is defined. The main assertion is that accomplishing target recognition tasks is equivalent to setting thresholds for the InDif. The applicability of the InDif to the performance of the human visual system (HVS) is shown both analytically, in very simple situations, and in computer calculations involving noisy images. Finally, a single framework for dealing with the HVS and artificial intelligence systems in target recognition applications is shown to result naturally from the InDif formalism.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
