A neural network system model for active perception and invariant recognition of grey-level images

A method for parallel-sequential processing of gray-level images and their representation which is invariant to position, rotation, and scale has been developed. The method is based on the idea that an image is memorized and recognized by way of consecutive fixations of moving eyes on the most informative image fragments. The method provides the invariant representation of the image in each fixation point and of spatial relations of features extracted in neighboring fixation points. A model of a neural network system for active visual perception and recognition of gray-level images has been developed based on this method. The experiments carried out with the model have shown that the system was able to recognize complex gray-level images in real time with invariance regarding position, rotation, and scale.<
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