A Model of Human Vision for Machines

Nigel D. Haig; Ian R. Moorhead

doi:10.1117/12.937819

22 January 1987 A Model of Human Vision for Machines

Nigel D. Haig, Ian R. Moorhead

Proceedings Volume 0728, Optics, Illumination, and Image Sensing for Machine Vision; (1987) https://doi.org/10.1117/12.937819
Event: Cambridge Symposium_Intelligent Robotics Systems, 1986, Cambridge, MA, United States

Abstract

The properties and characteristics of a new model of primate spatial vision are described and compared with human vision. The model employs a multiple level stack of graduated receptive field sizes whose sampling densities progressively decrease with level while maintaining a constant space-bandwidth product. The effect of this structure is to increase receptive field size with eccentricity, whilst retaining a constant number of samples per level, coupled with a set of octave-related spatial frequency filters at the fovea. Such an architecture, which exploits the Heirarchical Discrete Correlator of Burt (1981)17, correctly mimics the visual cortical mapping function of Schwartz (1983) 14 yet it has the valuable property that it can produce invariant responses to local changes in the size and position of features in the image. This architecture requires only local connections at each level, so producing the type of uniformity that is commonl observed as a feature of neural processing in the striate cortex (Rubel & Wiesel, 1974)1-1. An intrinsic characteristic of the model is the concept of "attention area", representing the spatial extent of each level in the stack, and this concept helps to explain the high efficiency of human visual search in terms of hierarchical scanning. Our model has been simulated on an INTELLECT image processor using many different natural scene inputs. Analysis of the results has revealed possible mechanisms for human visual accommodation and neural gain control, which have enabled us to program the simulator to control reliably the focus and gain of its own TV camera input. Simple mechanisms have also been programmed that allow rapid detection of scene changes and consequent shift of attention, together with smooth pursuit of targets through natural scenes. While the simulation is slow, being a serial manifestation of a parallel original system, it has demonstrated the outstanding value of the data compression that is inherent in this form of architecture. The advantages of such a homomorphic system for intelligent machines are, we feel, obvious.

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Nigel D. Haig and Ian R. Moorhead "A Model of Human Vision for Machines", Proc. SPIE 0728, Optics, Illumination, and Image Sensing for Machine Vision, (22 January 1987); https://doi.org/10.1117/12.937819

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