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ECVision indexed and annotated bibliography of cognitive computer vision publications This bibliography was created by Hilary Buxton and Benoit Gaillard, University of Sussex, as part of ECVision Specific Action 8-1 The complete text version of this BibTeX file is available here: ECVision_bibliography.bib C. Bandera and F. J. Vico and J. M. Bravo and M. E. Harmon and L. C. Baird Residual {Q}-learning applied to visual attention ABSTRACT Foveal vision features imagers with graded acuity coupled with context sensitive sensor gaze control, analogous to that prevalent throughout vertebrate vision. Foveal vision operates more efficiently than uniform acuity vision because resolution is treated as a dynamically allocatable resource, but requires a more refined visual attention mechanism. We demonstrate that reinforcement learning (RL) significantly improves the performance of foveal visual attention, and of the overall vision system, for the task of model based target recognition. A simulated foveal vision system is shown to classify targets with fewer fixations by learning strategies for the acquisition of visual information relevant to the task, and learning how to generalize these strategies in ambiguous and unexpected scenario conditions.

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