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Recognition of images moving with respect to a constant background (possibly contaminated by a noise) may be considerably improved if a Neuronlike Network (NN) with specific dynamic properties is applied for preliminary data processing. Optimal design of a dynamic network containing thousands of interconnected, nonlinear elements requires development of special tools and algorithms. In our system, the optimization of the NN parameters for preliminary processing of moving images is performed using a model of the dynamic processes in the retina called ARTINA. A compromise between satisfactory contrasting and noise elimination defines a "quasi-optimal" structure of local connections in the NN. In this paper we present an experimental procedure (using ARTINA) for partial optimization of a NN performing contrasting and local features detection for images moving with velocities within a given range of values. A performance index for contrasting and edge enhancement is first selected. It is then shown that through appropriate selection of dynamic properties of the NN elements, it is possible to achieve a considerable (up to 10 times) improvement of the performance index. Proper selection of time constants, delays, and connection ranges results in a "cumulation" process. This process enhances contours of moving objects and improves detection of local features. A special optimization algorithm, using a selected performance index, permits the optimization both of the neuron time constants and of the structure of local connections defining the central and peripheral receptive fields. Relations between object speed, time constants describing the dynamics of the neuron, and dimensions of receptive fields are investigated. Results of the optimization and a discussion of possible methods for improving the preliminary dynamic image processing is presented.
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