Correlates of Attention in a Model of Dynamic Visual Recognition

Given a set ofobjects in the visual field, how does the the visual system learn to attend to a particular object of interest while ignoring the rest? In this paper, we attempt to answer these questions in the context of a Kalman filter-based model of visual recognition that has previously proved useful in explaining certain neurophysiological phenomena suchas endstopping and related extra-classical receptive field effects in the visual cortex. The resulting robust Kalman filter model demonstrates howcertain forms of attention can be viewed as an emergent property of the interaction between top-down expectations and bottom-up signals. Themodel also suggests functional interpretations ofcertain attentionrelated effectsthat have been observed in visual cortical neurons. Experimental resultsare provided to help demonstrate the ability of the model to perform robust segmentation and recognition of objects and image sequences inthe presence of varying degrees of occlusions and clutter. 1 INTRODUCTION The human visual system possesses the remarkable ability to recognize objects despite the presence of distractors and occluders in the field of view.