Machine vision is an active branch of artificial intelligence. An important problem in this area is the trade-off among efficiency, accuracy and computation complexity. The human visual system can keep watchfulness to the perimeter of a viewing field while at the same time focus on the center of the field for fine information processing. This mechanism of appropriate assignment of computing resources can reduce the demand for huge and complex hardware structure. Therefore, the design of a computer model based on the biological visual mechanism is an effective approach to resolve problems in machine vision. In this paper, a multi-layer neural model is developed based on the features of receptive field of ganglion in retina to simulate multi-scale perceptive fields of ganglion cell. The neural model can maintain alert on the outer area of the image while capturing and processing more important information in the central part. It may provide valuable inspiration for the implementation of real-time processing and avoidance of huge computation in machine vision.
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