Neurophysiological Based Methods of Guided Image Search

"Complex analysis of intelligence imagery is crucial to the missions of intelligence organizations, yet remains constrained by labor-intensive, time-consuming visual search of large volumes of imagery. Many algorithms have been developed to automaticallyidentify regions of interest in large, complex sets of imagery, yet the utility of such algorithms is limited by the fact that human analysts detect features in imagery with higher accuracy than existing methods. We propose to develop a new model of visualfeature detection, Neuronal Synchrony Model, based on neurophysiological models of temporal neuronal processing, to improve the accuracy of automatic detection of features of interest in complex natural imagery. The Neuronal Synchrony Model of imagefeature detection will be applied to accurately identify and highlight regions of images that contain target features, thus automating the labor-intensive, "scanning" portion of imagery analysis. The accuracy of the Neuronal Synchrony Model will be testedwith natural images containing visually controlled, synthetic targets as well as with natural targets using a variety of overhead imagery background and target types. The output of this effort will be a proof-of-concept demonstration of the effectivenessof this model in enhancing the speed and accuracy of interactive, guided visual search of representative imagery. Anticipated benefits of this effort are increased accuracy and speed of proc