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Abstract:
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 proved 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 in performing robust
segmentation and recognition of objects and image sequences in the
presence of varying degrees of occlusions and clutter.
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