Correlates of Attention in a Model of Dynamic Visual Recognition

Given a set of objects in the visual field, how does the the visual system learn to attend to a particular object of interest while ignoring the rest? How are occlusions and background clutter so effortlessly discounted for when recognizing a familiar object? In this paper, we attempt to answer these questions in the context of a Kalman filter-based model of visual recognition that has previously been useful in explaining certain neurophysiological phenomena such as endstopping and related extra-classical receptive field effects in the visual cortex. By using results from the field of robust statistics, we describe an extension of the Kalman filter model that can handle multiple objects in the visual field. The resulting robust Kalman filter model demonstrates how certain forms of attention can be viewed as an emergent property of the interaction between top-down expectations and bottom-up signals. The model also suggests functional interpretations of certain attention-related effects that have been observed in visual cortical neurons. Experimental results are provided to help demonstrate the ability of the model to perform robust segmentation and recognition of objects and image sequences in the presence of varying degrees of occlusions and clutter.