Engineering topics
Publications and source records attributed to Yellott, John I., Jr..
Neural-network-like models of receptor position learning and interpolation function learning are being developed as models of how the human nervous system might handle the problems of keeping track of the receptor positions and interpolating the image between receptors. These models may also be of interest to designers of image processing systems desiring the advantages of a retina-like image sampling array.
In a previous paper, a class of constant volume (CV) operators designed to maximize spatial resolution in the presence of photon noise was introduced. In this paper, the statistical properties are derived of the output images created by CV operators applied to photon-noisy versions of input images consisting of edges, spots, and gratings. The results are used to calculate the input image parameters that would permit any test image to be discriminated from a uniform field with a given level of reliability and consequently to compare the predictions of a CV operator model with psychophysical data. These results also allow the potential usefulness of CV operators in artificial image processing applications to be assessed.