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- W2128565514 abstract "The purpose of the present paper was to implement two computational models of structure-from-stereopsis comparing the computational utility of interval and distributed codings of image disparity. Interval codings represent disparity in discrete ranges, whereas distributed codings represent disparity using overlapping tuning curves similar to the response profiles of neurons found in the visual cortex of primates. We relate this also to recent neurophysiological work indicating that binocular neurons may be tuned to the relative phase of luminance input to the left and right eyes. A distributed coding of disparity has been shown previously to provide a good model for human stereoacuity. In this paper we show that it is also advantageous for solving the structure-from-stereopsis problem. Separate sets of neural network simulations were undertaken to learn a mapping function from (1) interval and (2) distributed disparity codings to a three-dimensional surface representation. Example pairs of disparity codings and surface representations were used to adjust the weights on a radial basis function network to learn the mapping function for the example surfaces. The generalizability of the learned mapping function was tested with novel surfaces. While the networks performed equally well (perfectly) on the learned surfaces, the distributed disparity coding networks generalized better to novel surfaces than did the interval code." @default.
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- W2128565514 date "1994-09-01" @default.
- W2128565514 modified "2023-09-23" @default.
- W2128565514 title "NEURAL NETWORK SIMULATIONS OF DISTRIBUTED VERSUS INTERVAL DISPARITY CELL CODINGS" @default.
- W2128565514 doi "https://doi.org/10.1142/s0218339094000234" @default.
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