Models For Neural Matched Filters In Vision

A central question in cognitive psychology is how do animals recognize visual objects that they have learned? That is, how do they recall the name and the learned properties of an image on the retina? The image might be a shape, a letter of the alphabet, a face, an automobile, etc. Image properties and features such as relative size, shape, color, and contrast all must figure in the identification/recognition process. Clearly, information stored in the CNS is compared with the incoming information to arrive at a match or probable match. This section examines a simple, highly-speculative model for object recognition, i.e., the neural matched filter. Before examining the application of a matched filter model to the recognition of elementary visual objects, it is important to note that application of the matched filter concept, as used in communications, requires the assumption of a linear system. However, an animal's CNS is in general quite nonlinear, although piecewise linearity might be argued under certain input operating conditions.

The matched filter was originally developed as a tool for communications in the time domain (Schwartz, 1959). It permits the design of the statistically optimum filter to allow detection of a singular event (such as a pulse or small group of pulses) combined with broadband, Gaussian noise. Curiously, the exact implementation of matched filters in the time domain is not possible because of causality. However, the exact implementation of realizable matched filters in the space domain is possible. They can also be extended to two dimensions and be discretized (Papoulis, 1968). The big question is, does pattern recognition in animal visual systems rely on some form of matched filter operation? The design of a spatial matched filter (SMF) model is now examined with a view to evaluating it as a candidate visual signal-processing strategy.

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