Automatic target prediction and subtle gaze guidance for improved spatial information recall

Humans rely heavily on spatial information to perform everyday tasks. Developing good spatial understanding is highly dependent on how the viewer's attention is deployed to specific locations in a scene. Bailey et al. [2009] showed that it is possible to influence exactly where attention is allocated using a technique called Subtle Gaze Direction (SGD). The SGD approach combines eye tracking with subtle image-space modulations to guide viewer gaze about a scene. The modulations are presented to peripheral regions of the field of view, in order to attract the viewer's attention, but are terminated before the viewer can scrutinize them with their high acuity foveal vision. It was observed that subjects who were guided using SGD performed significantly better in recollecting the count and location of target objects, however no significant performance improvement was observed in identifying the shape of the target objects [Bailey et al. 2012]. Also, in previous studies involving SGD, the target locations were manually chosen by researchers. This paper addresses these two limitations. We present a novel technique for automatically selecting target regions using visual saliency and key features in the image. The shape recollection issue is solved by modulating a rough outline of the target object obtained using an edge map composed from a pyramid of low spatial frequency maps of the original image. Results from a user study show that the influence of this approach significantly improved accuracy of target count recollection, location recollection, as well as shape recollection without any manual intervention. Furthermore our technique correctly predicted 81% of the target regions without any prior knowledge of the recollection task being assigned to the viewer. This work has implications for a wide range of applications including spatial learning in virtual environments as well as image search applications, virtual training and perceptually based rendering.